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International Journal of
ARCHITECTURE ENGINEERING and CONSTRUCTION
Editor-in-Chief
Xueqing Zhang
Hong Kong University of Science and
Technology
Hong Kong
Architecture,
Engineering and
Construction
Editorial Advisory Board
Simaan M. AbouRizk
University of Alberta, Canada
Bryan T. Adey
ETH Zurich, Switzerland
Thomas Bock
Technical University of Munich, Germany
Honorary Editor
Hojjat Adeli
Ohio State University
United States of America
Makarand Hastak
Purdue University, United States of America
Timothy J Ibell
University of Bath, United Kingdom
Edward J Jaselskis
North Carolina State University, United States of America
Kiyoshi Kobayashi
Associate Editor for Architecture
Ewelina Woźniak-Szpakiewicz
Cracow University of Technology
Poland
Associate Editor for Engineering
Shunbo Zhao
North China University of Water
Resources and Electric Power
China
Associate Editor for Construction
Boong Yeol Ryoo
Texas A&M University
United States of America
Kyoto University, Japan
Thomas Kvan
University of Melbourne, Australia
Kincho H Law
Stanford University, United States of America
Christopher K Y Leung
Hong Kong University of Science and Technology, Hong Kong
Ali Maher
Rutgers University, United States of America
Campbell R. Middleton
University of Cambridge, United Kingdom
Peter W. G. Morris
University College London, United Kingdom
George Ofori
National University of Singapore, Singapore
Feniosky Pena-Mora
Columbia University, United States of America
Qinghua Qin
Australian National University, Australia
Klaus Rueckert
Technical University of Berlin, Germany
Surendra P. Shah
Northwestern University, United States of America
Miroslaw Skibniewski
University of Maryland, United States of America
Jinbo Song
Dalian University of Technology, China
Corrado Lo Storto
University of Naples Federico II, Italy
Nobuyoshi Yabuki
Osaka University, Japan
General Information
International Journal of Architecture, Engineering and Construction
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International Journal of
Architecture,
Engineering and
Construction
Volume 5, Number 1
Research Papers
Managing Editor
Shuibo Zhang
Tianjin University
China
Assistant Editors
Yashuai Li
1
Integrating AHP-Fuzzy Model for Assessing Construction
Organizations’ Performance
Emad Elwakil
13
Performance Evaluation of Vertical Gardens
Ratih Widiastuti, Eddy Prianto and Wahyu Setia Budi
21
Modeling Infrastructure Bridges Maintenance Work Zones
Mohamed Marzouk and Kouzal El Banna
29
Organizational Competencies and Project Performance Tool
(OCPPT©): Evaluating Construction Project Competencies
and Performance
Moataz Nabil Omar and Aminah Robinson Fayek
44
Investigating and Ranking Labor Productivity Factors in the
Egyptian Construction Industry
Michael Gerges, Ograbe Ahiakwo, Georgios Kapogiannis,
Messaoud Saidani and Danah Saraireh
53
Compensation Mechanism for Early Termination of Highway
BOT Projects Based on ARIMA Model
Song Jinbo, Yanan Fu and Ousseni Bagaya
Beihang University, China
Marie Noel Bernal
International Association for
Sustainable Development and
Management
Canada
March 2016
Vol 5, No 1, March 2016, 1-12
Integrating AHP-Fuzzy Model for Assessing Construction
Organizations’ Performance
Emad Elwakil∗
School of Construction Management, Purdue University, 433 Knoy Hall of Technology, 401 N. Grant Street,
West Lafayette, Indiana, USA, 47907
Abstract: Organizations performance assessment is a critical aspect in today’s project management research.
Construction organizations face difficulties in performance assessment, stemming from the uncertain, fragmented, and unique nature of construction industry. Most of the research neglected the different perspectives of
construction organizations’ functional units when assessing their performance. Therefore, the goal of this research is to design a comprehensive performance assessment model through identifying and ranking a set of
critical success factors (CSFs). Four assessment models are developed to reflect the different perspectives of
four functional units in construction organizations. Analytical Hierarchy Process and Fuzzy Expert System
are used for data analysis and models development. The research findings indicate that the CSFs factors in
construction organizations have different priorities and weights according to different functional units. The
validation results range from 84% to 93%. Overall, performance assessment models will benefit organizations
in assessing performance according to the perspectives of different individuals.
Keywords: Organizational performance, performance assessment models, organizational functional units, analytical hierarchy process, hierarchical fuzzy expert system
DOI: 10.7492/IJAEC.2016.001
1 INTRODUCTION
tation technique, using the relative importance index
approach to rank the classified categories based on
their perceived importance. Babatunde et al. (2016)
used the critical success factors (CSFs) to develop a
process maturity and determine the current maturity levels of stakeholder organizations in public-private
partnership (PPP). The study found that the maturity
of CSFs made PPP projects successful. Wibowo and
Alfen (2015) identified 30 government-led critical success factors (CSFs) from both the meso and micro levels in public-private partnership (PPP) infrastructure
development, measured the importance of these factors, and evaluated the government performance within the Indonesian context. Dang and Le-Hoai (2016)
used the critical success factors (CSFs) to identify the
correlation between critical success factors (CSFs) and
Design-Build projects’ performance measured by key
performance indicators (KPIs). Nilashi et al. (2015)
highlighted the importance levels of interdependency
among the CSFs which has rarely been explored in the
prior studies. In this study, most influential factors in
successfully completing construction projects are used
to develop a new integrated model, multi-criteria construction projects CSF model.
Organizational performance is the main driver for success and profit, thus, making performance assessment
a necessity to any organization.
Moreover, performance assessment of construction organizations
is more challenging due to the complex, fragmented nature of construction organizations (Abraham
2002). The factors that affect organizations’ performance must be fully understood to achieve success (Kaplan and Norton 1995), as well as diversity in the perception of success factors between
different functional units within the same organization. A Critical Success Factors (CSFs) evaluation
has been found to be the most appropriate methodology to assess and evaluate the organization’s performance in order to achieve its main goal of developing a comprehensive monitoring system that contains
corporate-wide indicators of success (Holohan 1992;
Radwan et al. 2015; Rathore et al. 2015; Radwan
and Elwakil 2015).
Tsiga et al. (2016) identified 58 success factors that
were then classified into 11 groups. These factors were
then tested within the space industry using an elici*Corresponding author. Email: [email protected]
1
Elwakil/International Journal of Architecture, Engineering and Construction 5 (2016) 1-12
Many research efforts have been done to determine
success factors in construction organizations. However, most of the conducted research only focuses on the
construction project level rather than the organizational level (Barakat et al. 2015). Nevertheless, there are
various methods and approaches to determine the key
success factors of organizations. The most commonly
used approach is the utilization of questionnaires and
interviews with technical personnel and industry professionals. Overall, the need for determining success
factors has increased as it can be an indicator for organizational performance and also can be used to assess
and improve performance. The goal of this study is
to understand the differences between different functional units’ perspectives in construction organizations
and how the functional units’ perception affects the
construction organizations’ performance through the
following objectives:
quantitative modeling techniques to obtain more firm
results.
(Rockart 1978) identified critical success factors as
critical areas where high performance is important, as
these factors decide the success of an organization. In
addition, CSFs are the actual steps taken to succeed.
Special attention and concern should normally be given to these areas, as these areas can decide the present
and the future success of an organization based on its
performance (Boynton and Zmud 1984). For the purpose of this research, eighteen critical success factors
were identified as the factors that impact construction
organizations’ success. Elwakil et al. (2009), Zayed
et al. (2012) classified these factors as the following:
1. Administrative and legal factors include the subfactors: clear vision, mission and goals, competition strategy, organizational structure, political
conditions, and number of full time employees;
1. Identify and study the key success factors for the
construction industry at the organizational level;
2. Technical factors include the sub-factors: usage
of international aspects, the availability of knowledge, usage of knowledge, business experience
(number of years), and product maintenance;
2. Analyze and determine the weights and impact
of critical success factors perceived by functional
units on the organizational performance;
3. Build functional units based assessment models
for construction organizations’ performance;
3. Management factors include the sub-factors: employee culture, environment, employee compensation and motivation, applying total quality
management, and training;
4. Validate the developed models.
2 BACKGROUND
4. Market and finance factors include the subfactors: quick liquid assets, feedback evaluation,
research and development, and market conditions/customer engagement.
Success definitions have evolved over the past decade
in the construction industry; and, it is mostly defined
as the overall achievement of the organization’s goals
and expectations. However, success can be assessed
differently from one individual to another according
to their perspectives. Elwakil et al. (2009) identified
eighteen significant success factors for the performance
assessment of construction organizations. A regression
model based on critical success factors was developed
to assess construction organizations’ performance. The
obtained data were analyzed using a back propagation
model of artificial neural networks (ANN), which was
used to determine the significance of various success
factors. Zayed et al. (2012) identified nine critical
success factors as the most significant to develop an
assessment model for organizational performance. Artificial neural network (ANN) model was used to assess
the most significant success factors, as ANN provided
the contributing weight of each factor after the completion of the training process. However, there has
been a lack of research on the assessment of construction organization performance based on the different
functional units’ perspectives. The available research
failed to consider how the different units perceive the
success factors differently and thus can affect the performance assessment. Also, the previous research did
not consider the integration of different qualitative and
3 ANALYTIC HIERARCHY PROCESS
(AHP)
Analytic hierarchy process (AHP) is a multi-criteria decision making method (Goepel 2013). It is a theory of
quantifying intangible factors that affect the decision
making process (Zayed and Halpin 2004). It is also
a non-complicated technique that attempts to simulate the human decision making process (Saaty 2008).
Furthermore, AHP mainly works through a sequence
of pairwise comparisons between the factors that influence the decision making process (Al-Barqawi and
Zayed, 2008). The of significance AHP stems from its
ability to quantify and compare the subjective or qualitative variables (Goepel 2013). In the construction
industry, it is very difficult to subjectively evaluate the
performance of workers or the effect of certain variations on the organizational performance. Therefore, a
need for a method that converts subjective opinions of
qualitative numbers is a necessity.
AHP has been implemented in many different research fields. Al-Harbi (2001) applied AHP as a decision making tool for project managers. It also has been
2
utilized in the selection process of contractors for speas follows:
cific projects based on qualification criteria (i.e. experiCI = (λmax − d)/(d − 1)
(1)
ence, financial stability, quality, resources, and equipwhere λmax is the maximum eigenvector and d is
ment). For instance, Korpela and Tuominen (1996)
the matrix dimensions.
presented an integrated approach to the site selection
process of a warehouse. The study considered both
6. Consistency ratio (CR) is then calculated as folquantitative and qualitative aspects in the selection
lows:
process. Also, Zhao et al. (2004) applied AHP techCR = CI/RI
(2)
niques to simulate and evaluate the relative weighting
where CI is the consistency index and RI is the
of the fire safety attributes of buildings.
random index, which is the average C.I. of sets of
A great feature of AHP is its flexibility to be intejudgments (from a 1 to 9 scale) for randomly gengrated with different modeling techniques like multiple
erated reciprocal matrices, to indicate whether
linear regression, fuzzy logic, artificial neural network,
the estimates are closer to being consistent or to
etc. These techniques enable researchers to extract
being randomly assigned. According to (Saaty
benefits from all of the combined methods, and, hence,
1990), if the CR is more than 10%, then the reachieve the ultimate goal in a more comprehensive way.
sults are inconsistent. Thus, the values should be
Three main principles form the basis of solving a
changed until CR is verified;
problem: 1) developing the hierarchies; 2) setting the
priorities; 3) ensuring logical consistency within the
factors. To develop an AHP model, however, six steps
4 FUZZY EXPERT SYSTEMS
are required (Al Khalil 2002; Vaidya and Kumar 2006;
Saaty 1990; Saaty 2008):
Zadeh (1965) introduced fuzzy logic as a powerful modeling technique that can be used to understand the
1. Identify the problem to be solved or the purpose uncertainty of human thinking. Fuzzy techniques have
of the model;
been widely utilized in several research studies over the
2. Identify the criteria that influence the behavior past decade, and they have the ability to virtually conof the factors that contribute to problem solving; nect humans to computers through analyzing linguistic
inputs to stem numerical outputs (Chan et al. 2009).
3. Assign the relative weights of the factors and subTraditionally, a set of inputs has sharp, crisp boundfactors in each category using pairwise compararies, where elements are either in or out of a set, and
isons between each pair in the same hierarchy.
the ranking of a membership of a variable is either zero
This requires an (n-1)/2 comparisons, where n
or one (Nguyen 1985). However, in the real world, inis the number of factors with the consideration
formation is mainly ambiguous and incomplete. Therethat diagonal elements are equal to “1” and the
fore, fuzzy logic comes in hand as elements are allowed
other elements will simply be the reciprocals of
to have partial memberships ranging from zero to one
the earlier comparisons. A comparison matrix is
(i.e. 0 is no membership and 1 is full membership)
developed as follows: 

(Fayek and Sun 2001).
1
x y
Recently, the fuzzy approach has become popular
1
z
Factor Comparison =1/x
among
construction management researchers as fuzzy
1/y 1/z 1
logic
theory
has enabled the handling of complex probwhere x, y, and z are numbers (integers or nonlems
in
real
world systems, which are mainly defined
integers);
through linguistic statements. The popularity of fuzzy
4. Perform calculations for consistency check, if the expert systems is summarized as follows: 1) the knowldeveloped matrix is consistent, then the weight edge based systems can summarize the human expervector for all of the qualitative factors will be cal- ts’ experiences; 2) the fuzzy linguistic descriptors are
culated by elevating the matrix to different pow- most commonly used by humans, which are inexact
ers and normalizing the matrix (i.e. converting and qualitative; 3) these systems can deal with inexact
the summation of each column to be one) at these figures and numbers; 4) they provide reasonable decipowers. The produced normalized column is the sions even if the input knowledge is incomplete; and, 5)
eigenvector. This process is repeated until the educated assumptions can be used to complete the lack
eigenvector solutions are not changed from the of knowledge in some cases. Chan et al. (2009) catprevious iteration (i.e. up to four decimal places egorized the application related to construction man0.0001). If the matrix is not consistent, it has to agement using fuzzy logic research into four categories:
be returned to the expert to adjust the response decision making, e.g. (Kazaz et al. 2014; Lin and Chen
and to be consistent with the values. Once it is 2004); performance assessment, e.g. (Fayek and Oduba
consistent, step three is repeated.
2005; Zhang et al. 2004); evaluation and assessment,
5. Consistency Index or eigenvalue (CI) is the calcu- e.g. (Zayed 2005) and modeling, e.g. (Okoroh and
lated value used to check the matrix consistency Torrance 1999).
3
5 INTEGRATING AHP WITH FUZZY
EXPERT SYSTEMS
4. Apply Hierarchical Fuzzy Expert System (HFES)
after selecting the most significant CSFs to the
different functional units to build the assessment
model;
5. Develop a performance scale to assess the organizational performance based on the perspectives
of the functional units and how they perceive
the CSFs. The AHP is selected because it is a
knowledge-based oriented technique that requires
the experts’ opinions to accommodate the success
in assessing organizational performance;
6. The models are tested and verified in order to
check their robustness in assessing the performance of a construction organization. The different models are validated by applying the models
on actual data and by checking the consistency
of the output.
Yang and Chen (2004) illustrated that AHP creates
and deals with a much undetermined scale of judgment. Furthermore, AHP does not take into account
the uncertainty associated with the mapping of human
judgment to a number of natural language. Some drawbacks of using AHP solely are that the ranking of the
AHP method is rather imprecise and that the subjective judgment of perception, evaluation, and selection
based on the preference of decision-makers greatly influence the AHP analysis results. To overcome these
problems, several researchers integrate fuzzy logic with
AHP to improve the uncertainty. Moreover, numerous
input variables complicate the fuzzy model development as it accordingly increases the number of fuzzy
rules (Kazaz et al. 2014). If the number of factors is
high, the model development would be infeasible as the
number of fuzzy rules increases exponentially.
In a research study conducted at Purdue University
in 1995, Ersoz (1995) integrated AHP and fuzzy logic for productivity estimation; the study introduced a
new assessment approach which considered the subjective factors that influence productivity. Another study,
conducted by Zayed (2005), integrated the AHP and
fuzzy logic methods to develop a productivity index
model for piling process.
6 METHODOLOGY
The goal of this research is to develop and validate a
model for organizations’ performance assessment based
on the perceived value of the different functional units
within the organization. This goal will be accomplished
through fulfilling the following objectives. Figure 1
shows a graphical representation of the methodology.
1. Review the existing literature and previously developed models in order to identify the critical
success factors in construction organizations;
Figure 1. Methodology development
2. Consult experts to determine the weight of the
factors that contribute most to the organizational performance through the opinion of experts in
the construction industry. Also, identify the impact of each factor on the overall organization
performance;
7 DATA COLLECTION
A number of critical success factors (CSFs) were identified based on experts’ opinions and experiences. Four
main factors were identified as the main categories to
be included in the models (i.e. administration and legal, technical, management, and market and finance).
Eighteen sub-factors were included in the AHP models, but this number will be reduced in the fuzzy model
afterwards based on the significance of the factors to
the functional units. The idea is to identify the different perspectives of each functional unit within the
organization.
Data collection involved two main stages: 1) pairwise comparisons of the main factors and sub-factors
3. Develop a performance assessment model for the
four different functional units in the organizations based on the CSFs using the analytic hierarchy process (AHP) and Hierarchical Fuzzy Expert System (HFES). The AHP will be applied
using the data collected from the experts and the
pairwise comparisons. AHP will be used to determine the weights of the factors and to select
the most significant factors to the four functional
units’ models;
4
and 2) identifying the impact of each factor in the performance of the organization. A questionnaire was administered to different functional units in construction
organizations to reflect their experience and the company performance from their perspectives. One hundred
fifty (150) questionnaires were sent to basic functional units in construction organizations worldwide (i.e.
Canada, Egypt, France, Greece, Germany, USA, Saudi
Arabia, and United Arab Emirates). The returned surveys from the different respondent groups are shown in
Table 1.
for the main factors and sub-factors. Using pairwise
comparisons allows the individuals to express the relative importance of one factor over another. AHP analysis is applied to determine the factors weight (Wi )
and sub-factors weight (SWij ) of each factor based
on the individuals’ input. Consistency of the pairwise comparison matrices is tested using Equations
1 and 2 above. The CR values are all less than 10
percent, which is the acceptable range (Saaty 2008).
All of the matrices that were received from experts are
consistent. This step is repeated for all of the respondents in each of the four assessment models.
Step 3 - Aggregated priority weights: Priority
8 ORGANIZATIONS’ PERFORMANCE
weights aggregation follows the consistency analysis.
ASSESSMENT MODEL
Where the aggregated weight of each sub-factor is calThe AHP model is designed to identify the weights of culated by multiplying the sub-factor weight (SWij )
the CSFs as perceived by the functional units. The by the corresponding main factor weight (Wi ) of the
significant CSFs are identified for each model and then same category. Accordingly, priority can be established
the HFES model is designed to assess the performance based on the overall weight using Equation 3 as follows:
of the construction organizations from the perspectives
ASWij = Wi × SWij
(3)
of four different functional units (i.e. directors, senior
engineers, project managers, and cost engineers).
Where ASWij is the aggregated weight of the subfactor, (Wi ) is the weight of the main factor, and
8.1 CSFs Determination (AHP Model)
(SWij ) is the weight of sub-factor j in the ith factor.
There are several methods to assess the significance
Table 2 shows the results of the aggregation process
of independent factors affecting the performance of a
based
on the average values of the collected matrices of
dependent criterion. In this research, AHP method
the
directors,
senior engineers, project managers, and
is used to assess the most significant success factors
cost
engineers
functional units’ models.
because the AHP method provides the contributing
Step
4
CSFs
selection: The significant CSFs are seweight of each factor after completing the model trainlected
based
on
their
average ASWij %, as the selecting process. Therefore, in the following sub-sections,
ed
CSFs
are
equal
to
or
above the average of the total
details regarding these contributing weights are preweights
of
the
factors
of
their
corresponding functional
sented. The following steps are a guide for training the
unit.
Table
2
shows
the
selected
CSFs marked with a
AHP model (Al-Barqawi and Zayed 2006).
bullet.
Nine
CSFs
are
identified
for
each model, exceStep 1 - Setting up the factors hierarchy: The factors
pt
for
the
senior
engineer’s
model
where
ten CSFs are
that affect the organization’s performance are divided
found
to
be
above
the
average.
The
factors
that are not
into three main levels as shown in Figure 2. Level one
selected
are
illuminated
from
the
fuzzy
model
and not
represents the main objective of the factors (i.e. assessconsidered
in
the
assessment
process.
It
is
clear
from
ment of organization’s performance). Level two repreTable
2
that
the
administrative
and
legal
category
is
sents the four main factors (i.e. administration & legal,
totally
illuminated
from
the
senior
engineers’
model
actechnical, management, and market & finance). While
level three represents the AHP model, sub-factors, or cording to the weights of the sub-factors. This proves
the overall eighteen critical success factors (e.g. organi- the robustness of the AHP model, as engineers normalzational structure, employee culture, environment, and ly tend to focus more on the other factors rather than
business experience). This step is identical in the four administration.
functional units’ models.
Figure 3 shows the relative importance of the subStep 2 - Assigning priorities, establishing a priority factors in each model. Where 18 represents the highest
vector (eigenvector), and response consistency analy- ranking and the most important factor to the functionsis: In this step, the functional units’ individuals and al unit, while 1 represents the lowest ranking and the
industry experts provide pairwise comparison matrices least important factor to the functional units.
Table 1. Survey return data
Directors
Number of responses
Response rate, proportion (%)
Total
12
19%
Functional unit
Senior
Project
engineers managers
20
21
32%
33%
5
Country of origin
Cost
engineers
10
16%
63
Egypt
Canada
Other
31
49%
21
33%
11
18%
Figure 2. Hierarchy of the AHP model
Table 2. Aggregation process for CSFs
Administrative
& Legal
Technical
Management
Market
& Finance
Success factors
Directors
X1: Clear Vision, Mission, and Goals
X2: Competition Strategy
X3: Organizational Structure
X4: Political Conditions
X5: Number of Full Time employees
X6: Usage of International Aspects (ISO)
X7: Availability of Knowledge
X8: Usage of IT
X9: Business Experience (No. of years)
X10: Product Maintenance
X11: Employee Culture Environment
X12: Employee Compensation and Motivation
X13: Applying Total Quality Management
X14: Technical Training
X15: Quick Liquid Assets
X16: Feedback Evaluation
X17: Research and Development
X18: Market Conditions/Customer Engagement
ASWij %
6.28% •
6.14% •
5.68% •
5.16%
5.01%
4.29%
6.3% •
5.81% •
6.43% •
5.02%
5.42%
5.74% •
5.25%
5.32%
5.55% •
5.39%
5.26%
5.96% •
Selected CSFs
Senior
Project
engineers
managers
ASWij % ASWij %
4.96%
6.48% •
4.47%
6.11% •
4.61%
5.95% •
4.10%
5.08%
3.81%
4.94%
4.36%
4.36%
5.63% •
6.06% •
5.76% •
5.86% •
6.03% •
6.17% •
5.29% •
5.16%
5.06%
5.29%
6.01% •
5.75% •
5.19% •
5.06%
6.16% •
5.78% •
5.71% •
5.43%
5.38% •
5.49%
4.71%
4.98%
6.14% •
6.04% •
Note: • means “above average aggregated weights”
Figure 3. Ranking of the CSFs among the functional units
6
Cost
engineers
ASWij %
6.54% •
6.02% •
5.86% •
5.39%
5.02%
4.38%
6.06% •
5.71% •
6.43% •
5.38%
5.35%
5.76% •
4.97%
5.54%
5.58% •
5.23%
4.85%
5.95% •
8.2 Model Implementation (HFES)
resented by those shapes. The factors were evaluated
on a 0-10 scale and assigned a number of membership
functions (MFs) ranging from five to two MFs. In this
study, only a representation of membership functions
was presented. Figure 5 shows an illustration of the
“clear mission, vision, and goals” factors membership
functions used in this study.
The hierarchical fuzzy expert model (HFES) consists of four main sub-models with the exception of the
senior engineers’ model which contains only three submodels. These models are correspondent to the four
categories of the critical success factors. Finally, the
last sub-model combines the results of the later four
sub-models in order to generate the organization performance assessment. The crisp defuzzified results of
the four sub-models (i.e. administrative & legal, technical, management, and market & finance) are combined together through the organization performance
model. This process is done for the four functional
units separately, as the result would be four different
performance assessment models. Figure 4 explains the
full view of one of the fuzzy models in this study (i.e.
directors’ model).
Figure 5. Clear mission, vision, and goals
membership function
Step 2 - Input Variables: Numerous input variables
complicate the fuzzy model development as it accordingly increases the number of fuzzy rules (Kazaz et al.
2014). If all of the eighteen factors were considered
in one fuzzy expert system model, the model development would be infeasible as the number of fuzzy
rules increases exponentially. If a complete rule base
was created for a fuzzy expert system with eighteen
input variables and each variable had three membership functions, the number of rules required would be
318 (approximately four billion rules). This reasoning
supported the use of the AHP technique in order to reduce the number of fuzzy and criteria. As a result, the
largest number of rules was 27 rules in the sub-models.
Table 3 shows the input variables and their corresponding linguistic variables and the numerical scale used for
the directors’ model.
Step 3 - Output Variables: The construction organization performance assessment was the main goal
of this research. Moreover, the membership function
of the output included five fuzzy linguistic descriptors
(i.e. poor, fair, good, very good, and excellent) and the
performance was ranked out of 100 to increase the sensibility. The scale shown in Figure 6 displays the scale
used to compare the fuzzy numbers. Because the model was developed for performance assessment, the fuzzy
sets were dependent, and there was an intersection between the sets and a sequential increase and decrease of
them. Hence the center of sums defuzzification method
was used in this research. Figure 7 shows the output
variable membership function.
Figure 4. Full view of the directors fuzzy model
in this study
Step 1 - Membership Functions Determination: On
the questionnaire, industry experts were asked to select
a range of numerical values that corresponded with the
linguistic states of both input and output factors in order to construct the initial membership functions. The
ranges, shapes, and values of the membership functions for each variable were designed according to the
information from the literature, as well as average respondents’ values. There are many forms of membership functions, such as triangular, trapezoidal, bellcurved, and sigmoidal functions. The factors’ membership functions are used to convert the crisp input data
(e.g. number of employees, years of experience, and usage of technical aspects) into fuzzy numbers. Because
this study focused on the qualitative aspects of the factors, only triangular and bell-curved shapes were used,
hence most membership functions were accurately rep-
Figure 6. Proposed performance assessment scale
7
Equivalent impact =
P
(f uzzy set × weight) of each f actor
P
× 10 (4)
f actor weights
Organization P erf ormance Assessment M odel
(7.5 × 34.67) + (5 × 34.05) + (9.72 × 31.28)
× 10
(34.67 + 34.05 + 31.28)
= 73.4
(5)
=
Figure 7. Organization performance membership
function
The crisp value from Equation 5 is then compared
to the performance scale in Figure 7 to identify the
linguistic term of the category.
Step 5 - Consequent Aggregation: Similar procedures
were established for the rest of the sub-models to finally aggregate the consequences of all of the factors and
to generate the final model for assessing the organizational performance. The same concept applied to
the four functional units’ models. However, due to the
selection of different factors based on the perception
of the different functional units, the models had different performance rules and ultimately different performance models. This process occurred after evaluating
each role in the knowledge base and before the defuzzification process. The output linguistic variable was
aggregated using a maximum mathematical operation
as in Equation 6. During this process, the maximum
membership value of any membership function is used
to abbreviate the membership function for later ease
of use in the defuzzification process (Fares and Zayed
2010).
Step 4 - Fuzzy Rules: The last step of the model
development is to establish the rules. Fuzzy rules are
the conditions of the model. The rules show the correlation between the input and output variables. The
number of fuzzy rules in each model is dependent on
the number of factors and the number of membership
functions (i.e. fuzzy sets). The rules consist of fuzzy
prerequisites or antecedents and fuzzy conclusions or
consequences. For example, in the directors’ model:
If the Clear Vision, Mission & Goals is very good (7.5);
And the competition Strategy is moderate (5);
And the organizational structure is very good (9.72);
Then the Administrative & Legal aspect of this organization is very good (73.4).
The words in bold are a description of the factors as
assigned by the experts. The numbers in parentheses
are the assigned crisp value (numerical value) from the
survey.
µR (x1 , x2 , x3 , ..., xn , y)
The sub-model equivalent impact (i.e. fuzzy numbers) shown in the previous example above can be calculated by simply calculating the total number of fuzzy
sets multiplied by the weight of each factor then divided by the sum of all factor weights in this sub-model as
shown in Equations 4 and 5. It is important to highlight that the differences between the four functional
unit models will stem from the different input factors
selected from the AHP model. Also, the weights of factors will be different, as each functional unit perceives
the factors differently.
N
= Vj=1
[µR j(x1 , x2 , x3 , ..., xn , y)]
where V represents the maximum operation and R
represents each of the membership functions of the
output (i.e. poor, fair, good, very good, and excellent). This operation is also applied to each of the
sub-models (i.e. administrative & legal, management,
technical, and market & finance).
Step 6 - Fuzzy Models Defuzzification: Converting
fuzzy consequents into crisp values can be performed
by several defuzzification methods. The center of sum
Table 3. Directors’ model input variables
Main Performance
Factors
Administrative &
Legal
Technical
Management
Market &
Finance
(6)
Sub-Factor
Linguistic Scale
Numerical Scale
Clear Vision, Mission & Goals X1
Competition Strategy X2
Organizational Structure X3
Availability of Knowledge X7
Usage of IT X8
Business Experience (no. of years) X9
Employee Compensation and Motivation X12
Quick Liquid Assets X15
Market Conditions/Customer Engagement X18
v. Good, moderate, v. Bad
High, moderate, low
High, low
High, moderate, low
High, moderate, low
Good, moderate, bad
High, moderate, low
Poor, fair, good,
very good, excellent
1-10
1-10
1-10
1-10
1-10
1-10
1-10
1-10
1-10
Organization Performance
8
1-100
Table 7. Assessment from a cost engineer’s
perspective
method is utilized in this research. In addition, the
center of sum method calculates the center of gravity
of each abbreviated membership function then averages
their weights by their area as a reference as shown in
Equation 7. This method is utilized for its ease to program, and it provides reasonable results.
Factors
Administration & Legal
Technical
Management
Market & Finance
X1, 8
X7, 8
X12, 8
X15, 4
Sub-factors
X2, 6
X3, 4
X8, 2
X9, 8
X18, 4
Good
Crisp organization perf ormance output =
CoA1 area1 + CoA2 area2 + ... + CoAn arean
(7)
area1 + area2 + ... + arean
10 VALIDATION OF THE DEVELOPED
where CoAn , geometric center of the area of the scaled
ORGANIZATIONAL
membership is a function and arean is the area of the
PERFORMANCE MODELS
scaled membership function n.
The accuracy of each model will be determined separately based on the output performance value with the
9 MODEL IMPLEMENTATION
actual value determined by the functional unit. From
the collected data, 20 percent of the responses from
A sample of construction organization performance as- each functional unit is selected randomly and kept assessment models for the directors, senior engineers, ide to be excluded from the model designing process in
project managers, and cost engineers are presented in order to validate the modeled values. All of the modeTable 4-7 respectively. According to the final result ls are logically and practically experimented to ensure
of the organizational performance assessment model efficiency in assessing the performance. After building
(OPAM), the performance of the organization can be all of the models, the validation dataset is utilized to
determined and assessed based on the input from the test the ability of the models to assess the organizadifferent functional units. Assessing performance can tional performance.
help organizations identify weaknesses and hence work
The utilized criterion for the numerical match of an
on developing them.
observation is the average percent error (APE) and
average accuracy (AC) of a model. If the calculated
percentage error is less than or equal to 20%, as this
research adopts five MFs for the output variable, each
MF represents a range of 20% of the possible values
(Fayek and Oduba 2005).
Table 4. Assessment from a director’s perspective
Factors
Technical
Management
Market & Finance
X1, 8
X7, 8
X12, 8
X15, 4
Sub-factors
X2, 6
X3, 4
X8, 2
X9, 8
The percentage of error will be calculated as shown
in Equations 8 and 9:
X18, 4
Good
AP E % =
Table 5. Assessment from a senior engineer’s
perspective
Factors
Technical
Management
Market &
Finance
Organization
Performance
X7, 8
X12, 8
X8, 8
X13, 10
X15, 8
X16, 8
Average Accuracy (AC%) = 100 − AP E
Sub-factors
X9, 4
X10, 8
X14, 8
(8)
(9)
where APE represents the average percent of error of
the model, AC represents the average accuracy percent
of the model, V1 is the outcome value, and V2 is the
actual value. Table 8 shows a sample of the validation
dataset being utilized, as well as the APE and AC for
the models.
X18, 8
Very good
From the table, the accuracy values for the directors,
senior engineers, project managers, and cost engineers
models are 91.5%, 84%, 90.8%, and 92.5% respectively. These values indicate that the obtained results are
satisfactory. When comparing the output validation
data from all models, the results show that the cost
engineer’s model is closer to the actual data than the
other models. Figure 8 graphically shows the difference
between the actual and modeled values of the overall
organization performance for the four developed models, which shows a very close pattern behavior.
Table 6. Assessment from a project manager’s
perspective
Factors
Technical
Management
Market & Finance
|V 1 − V 2|
× 100
(V 1 + V 2)/2
Sub-factors
X1, 10
X2, 8
X3, 8
X7, 8
X8, 10 X9, 8
X12, 10 X14, 8
X18, 8
Very good
9
Table 8. Models validation samples
Model
Directors
Senior
Engineers
Project
Managers
Cost
Engineers
Validation
cases
26
25
24
24
23
22
48
47
46
52
50
48
Actual
performance
85(excellent)
60(good)
85(excellent)
94(excellent)
75(very good)
75(excellent)
90(excellent)
85(excellent)
70(very good)
60(good)
95(excellent)
80(very good)
Modeled
performance
72(very good)
58(good)
87(excellent)
76(very good)
82(excellent)
78(very good)
91(excellent)
88(excellent)
75(very good)
58(good)
87(excellent)
74(very good)
APE
17%
2.5%
9%
20%
9%
3.3%
1.5%
3.4%
7%
2.5%
8.9%
8.1%
P
APE and
P
P
APE = 8.5% &
P
AC = 91.5%
P
APE = 16% &
P
AC = 84%
P
APE = 9.2% &
P
AC = 90.8%
P
APE = 7.5% &
P
AC = 92.5%
AC
Figure 8. Actual performance vs. modeled performance
11 CONCLUSION
Construction organization performance is dependent
on several success factors. However, there is a lack of
research that focuses on assessing construction organization performance based on the different functional
units’ perspectives and how they perceive the critical
success factors differently. The framework for a performance assessment model was proposed to assess construction organizational performance from the point of
view of several functional units. Analytic hierarchy
process (AHP) was utilized to assess the critical success
factors (CSFs) based on the input from the industry experts. In addition, AHP was utilized to assign weights
to the CSFs as they were perceived from the functional
units before building a hierarchical fuzzy expert system
(HFES) to assess the organization performance. Also,
HFES was utilized to develop four assessment models for the four functional units based on the different
perceptions of the factor weights.
The developed models were validated by comparing
the output to the actual data regarding the organization performance. The validation of the models had
satisfactory results of 93%, 84%, 91%, and 94% for
the directors, senior engineers, project managers, and
cost engineers, respectively. In addition, the assessment models that were based on the functional units’
perspectives were compared with each other to identify the differences in the perspectives perceived by the
different functional units and to possibly determine the
most accurate model for assessing construction organizations’ performance. As a result, future research will
10
apply the developed model and compare the different models developed by different modeling techniques
(i.e. ANN) or merge two techniques together to obtain
more accurate results (i.e. AHP and regression analysis). The end result of this future research will lead to
a new generation of specific and accurate company performance models and fully automated models/systems
that might partially replace the expert opinion techniques.
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Vol 5, No 1, March 2016, 13-20
Performance Evaluation of Vertical Gardens
Ratih Widiastuti1,∗ , Eddy Prianto2 and Wahyu Setia Budi3
1,2
Architecture Department, Faculty of Engineering, Diponegoro University
Tembalang-Semarang, 50275, Indonesia
3
Physics Department, Faculty of Mathematics and Sciences, Diponegoro University
Tembalang-Semarang, 50275, Indonesia
Abstract: This paper presents a study about vertical garden as one of the most popular greenery systems in the
modern era. The aims of this research are to study the thermal performance of the vertical garden in the office
building and the influence of weather parameter toward the thermal performance of vertical garden. The object
study was the application of vertical garden in Pertamina branch office building, Semarang, Indonesia. The
vertical garden model has been verified with a set of measurement tools that measured weather parameter and
thermal performance for both bare and vegetated façades. The measurement demonstrated that the plant layer
on the façades can effectively reduce the interior surface temperature on the façades. The average difference was
2.1◦ C. When the outdoor air temperature increased, surface temperature of vegetated façades also increased.
The effective thermal resistance of a plant layer gradually decreases when the air temperature rises. It can be
concluded that the performance of vertical garden is influenced by the weather around the building.
Keywords: Interior surface temperature, thermal reduction, weather condition, vertical garden
DOI: 10.7492/IJAEC.2016.002
1 INTRODUCTION
mal comfort based on the trees quantity in the outdoor
space such as streets, pedestrian or parks.
Recently, greenery aspect has spread widely as an arThis research studies about the thermal performance
chitectural element to design building façades and ro- of the vertical garden in the office building and the inofs. It becomes more popular along with rapid mod- fluence of weather parameter toward the thermal perernization that changed the existing ecosystems and formance of vertical garden.
replacing with hard materials such as asphalt, paving
and concrete which resulted temperature increase in
every day. Though not a new concept, greenery as- 2 LITERATURE REVIEW
pects in the building has increased the percentage of
greenery in urban built-up area and bring back the 2.1 What is Vertical Garden
vanishing urban green space (Wong et al. 2003).
Green wall or vertical garden is the term used to reOne of the most popular greenery systems is vertical fer to all forms of vegetated wall surfaces (Sharp et al.
garden. On the market, many kinds of vertical gard- 2008). Vertical garden is not only for building aesthetens are available and it is possible to distinguish them ic, but also provides a sustainable, energy saving, comaccording to their constructive technology and to the fortable and healthy environment for building occupant
type of green cover which can be grass or plants. The (Rashid et al. 2010). It is rooted into the ground, on
possibility of reducing heat transfer through the build- the wall or in modular panels attached to the façades.
ing envelope became a plus point of vertical garden It is also called a system to attach plants to civil en(Holm 1989).
gineering structures and walls of buildings or vertical
In Indonesia, research related to greenery aspects on greened façades are walls that are either partially or
the building as a passive cooling system for energy sav- completely covered with vegetation, and they have exing was so rare. Mostly the research discussed ther- uberant green looks (Yeh 2010).
*Corresponding author. Email: [email protected]
13
Widiastuti et al./International Journal of Architecture, Engineering and Construction 5 (2016) 13-20
Some plants are able to grow on walls by taking root
in the substance of the wall itself. Typical of these
are mosses, lichens, grasses and vines. For these to
grow successfully on walls and buildings, some kinds
of support structure is usually essential (Johnston and
Newton 2004).
According to growing method, vertical garden can
be classified as green façades and living wall system
(Dunnett and Kingsbury 2004; Köhler 2008). Green
façades are made up of climbing plants either growing
directly on a wall or in specially designed supporting
structures. The system of plant shoot grows up the
side of the building while being rooted to the ground.
Green façades use climbing plant attached directly to
the building surface or supported by cables or trellis,
as seen in Figure 1.
Figure 2. Living wall system illustration: (a)planter
box system, (b)foam system, (c)mineral
wool system
2.2 Thermal Benefits-Temperature Reduction
Figure 1. Illustration of green façade
On the other hand, living wall systems are constructed from modular panels which contain soil or other
artificial growing mediums, for example planter box,
foam, felt, geotextile, perlite and mineral wool, as seen
in Figure 2, the panels require hydroponic cultures using balanced nutrient solutions to provide all or part of
the plant’s food and water requirements (Sharp 2007;
Dunnett and Kingsbury 2004). This system usually
employs evergreen plants as small shrubs which do not
naturally grow vertically.
Plants, especially vertical greening systems can protect the building envelope against the sunshine and
freezing weather, which is beneficial for the thermal
behaviour of the building indoor as well as outdoor.
Vertical greening systems improve thermal insulation
capacity through external temperature regulation. The
extent of the savings depends on various factors such
as climate, distance from the sides of buildings, building envelope type and density of plant coverage (Wong
et al. 2010; Akbari et al. 1997). Therefore, applying vertical garden can influence both the cooling and
heating (Akbari et al. 1997).
Vegetation can play an important role in the topoclimate of towns and the micro-climate of buildings (Wong et al. 2010). Despite that, vegetation can dramatically reduce the maximum temperatures of a building by shading walls from the sun,
with daily temperature fluctuation being reduced by
as much as 50% (Dunnett and Kingsbury 2004).
The shading and the corresponding reduction of the
temperature, are the reason that climbers commonly used in Mediterranean areas against walls or as
a canopy over terraces (Hermy et al., 2005). Plant
canopies that shade buildings move the active heat
from the building envelope to leave (McPherson et al.
1988).
14
Another statement by (Akbari et al. 1997), said that
cooling energy potential of shade trees by reduction
of the local ambient temperature. Irradiance reductions due to plants can reduce energy for space cooling.
Vertical garden improves thermal insulation capacity
through external temperature regulation (Stec et al.
2005).
Since 1996, an observation has been conducted on
the surface temperature of vertical garden in different settings at the University of Toronto (Bass et al.
2003). The results have consistently demonstrated that
among the materials found in urban areas such as lightcoloured brick, wall and black surface, vertical garden has cooler surface temperature. After that, a new
round of testing was conducted comparing a vertical
garden with a light-coloured metal surface, which is
typically found on roofs to shelter equipment. The purpose was not only to compare the temperature of the
two surfaces (metal and leaf) but to also assess the
shading potential of a vertical garden.
3 MATERIALS AND RESEARCH
METHODOLOGY
3.1 Description of Vertical Garden Model
In this section the vertical garden will be described in
detail, including planting system, irrigation and plant
species.
The kind of vertical garden used is geotextile system
that consists of an aluminum structure, a PVC panel
installed on it, and felt layers. The plants are growing
in the plant pockets which are always irrigated. They
cannot grow indefinitely because of the limited pocket
space, this is why it is not appropriate to apply plants
with large thick roots.
A continuous watering system that functioning automatically is needed. At the top and side of the vertical
garden is a flexible pipe for the irrigation. The water
flow through the nozzle and the distance between each
nozzle is 15 cm. Frequency of water flow depends on
the season, weather conditions and local climatology
conditions and orientation of the façades.
The vertical garden is installed on an external not insulated wall. The material of the wall is the bricks and
the thickness is 15 cm included internal and external
plaster. The plants used in the vertical garden model
are combination of various types of plants. They are
Phalaenopsis sp., Dracaena warneckii and some of local climber plants. Detail of the vertical garden model
can be seen in Figure 3.
The shading effect of vertical greenery systems reduces the energy used for cooling by approximately
23% and 20% the energy used by fans, resulting in
an 8% reduction in annual energy consumption (Bass
et al. 2003). Because in the fact, more thermal energy
flows into the non-shaded walls due to direct exposure
to the sun and resulted in higher surface wall temperature (Papadakis et al. 2001). Thermal behavior and
effectiveness of vegetation covers with different average
absorption of solar radiation and diffusive properties 3.2 Experiment Desription
(Takakura et al. 2000).
The measurement on the experiment of the perforNevertheless, toward interior thermal comfort, ver- mance of vegetation-covered walls was conducted to
tical garden gives an effect of increasing air humidity, validate the vertical garden model. The experiment
which is create discomfort for building occupants, es- consisted of measuring the interior façade thermal perpecially in the evening (Widiastuti 2014).
formance of a building non-covered and covered with
Figure 3. Detail of vertical garden models
15
plants in the Pertamina office building in Semarang
city, Central Java, Indonesia, as seen in Figure 4.
One interior area of the façade oriented east, approximately 4 m above the ground, was selected for the
measurements of vertical garden performance and other interior area, approximately 10 m above the ground,
in the same oriented façade was selected for measurement of bare wall (non covered vegetation), as seen in
Figure 5. The interior spaces are non air-conditioned
office space.
The experiment was conducted during one day in
October 6, 2013; from early morning (06:00 am) until evening 18.00 (06:00 pm). The measurements were
collected at the 1 hour time intervals. The weather
conditions during the experiment are summarized in
Table 1.
The following parameters were measured as individual points (variables) during the experiment:
1. Outdoor air temperature;
2. Surface temperature of the interior wall behind
the bare façade;
3. Surface temperature of the interior wall behind
the vegetated façade;
4. Relative humidity;
5. Wind speed near the façade.
The indoor-outdoor air temperature, relative humidity and wind speed near the façade were measured 30 cm
from the façade using 4 in 1 Environment Tester LM8000. Wind speed measurements were made at a single
Figure 4. Location of experiment site, Pertamina branch office building, Semarang
Figure 5. Exterior side of field measurement
16
Table 1. Weather conditions during the experiment
Values of weather condition
Highest values
Average values
Lowest values
Outdoor air temperature (◦ C)
35.2
33
29.5
point near vegetated façade. The surface temperatures
were measured using an infrared-surface thermometer.
Instruments are visible in Figure 6.
Relative humidity (%)
64.5
62.2
58.2
Wind speed (m/s)
1
0.5
0.3
4 DISCUSSION SURFACE
TEMPERATURE AND RESPOND TO
WEATHER
4.1 Respond to Relative Humidity
When the relative humidity of air is low, plants significantly decreased the rate of evaporation and made temperature increased. Upon the relative humidity is high
(at 14.00-18.00), the rate of evaporation from plants
greatly increases. Its use heat from the air to evaporate
water. Automatically, its will reduce surface temperature (at 14.00-18.00). It can be said that, decrease of
interior surface temperature equal to relative humidity.
The area of façade covered with vertical garden cools
better at higher humidity levels than bare façade, can
be seen in the Figure 8.
Figure 6. Measurement instruments: (a) infrared
surface thermometer, (b) 4 in 1 environment tester LM-8000
4.2 Respond to Outdoor Air Temperature
Data collecting was done by two assistants at the
same time in every 1 hour.
3.3 Experimental Results and Analysis
The experimental day was in sunny condition. Table
2, show the average, maximum and minimum values of
the measured thermal properties. Bare and vegetated
façade temperature measured can be seen in Figure 7.
The average interior surface temperature of the bare
façade was 32.7◦ C, while of the vegetated façade was
30.6◦ C.
The interior surface temperature of the vegetated
façade was always lower than bare façade (mean difference is 2.1◦ C). The peak interior surface temperature of
vegetated façade occurred around 15.00 (at 3:00 pm),
(32.15◦ C), while the bare façade occurred around 14.00
(at 2:00 pm) (34.8◦ C). This can be explained as thermal lag of the façade, which appears approximately
during one hour. Although the thermal lag was short
and it was not a focus of this study, it should be noted
that the effect of this thermal lag beneficial to reduce
cooling loads during peak hours.
The interior façade surface temperatures generally increased in response to increasing air temperatures, Figure 9. Even though surface temperature of the bare
façade was higher and closely matched with outdoor
air temperature, but at higher air temperatures, the
façade plant layer was also less effective in cooling the
interior façade surface temperature. It can be seen that
when outdoor air temperature increase, surface temperature of vegetated façade increase too. Therefore,
it can be concluded that the effective thermal resistance of a plant layer gradually decreases when the air
temperature rises.
4.3 Respond to Wind Speed
Wind can improve the micro-climate and has a specific effect in the building planning. Increasing wind
speed aspect made the interior façade surface temperature decreased. It was happened because of heat flux
through convection. The faster air movement then
the greater heat released (Frick and Suskiyatno 2007).
Though, wind speed was low (average 0.5 m/s), but
Table 2. Measured thermal properties of the bare and vegetated façade
Measured façade properties
Bare wall interior surface temperature (◦ C)
Vegetated wall interior surface temperature (◦ C)
Difference in interior surface temperatures (◦ C) (bare vs vegetated wall)
Temperature difference between outside air temperature and
interior surface bare façade (◦ C)
Temperature difference between outside air temperature and
interior surface vegetated façade (◦ C)
17
Maximum
34.8
32.15
4.4
Average
32.7
30.6
2.1
Minimum
29.9
28.5
0.2
0.5
0.3
0
4.8
2.4
0.3
Figure 7. Temperature measurement of bare and vegetated façade
Figure 8. Temperature measurement of bare and vegetated façade respond to relative humidity
Figure 9. Temperature measurement of bare and vegetated façade respond to outdoor air temperature
18
Figure 10. Temperature measurement of bare and vegetated façade respond to wind speed
when the wind speed increased (from 0.4 m/s to 0.8
m/s and from 0.8 m/s to 1.0 m/s), the reduction in
interior façade surface temperature between bare and
vegetated façade also increased, can be seen in Figure
10.
Since wind speed was low, vertical garden layer will
act as a buffer that keeps wind from moving along
on the building surface. Stagnant air made insulating effect and significantly reduced the amount of heat
transfer in the building. As a consequence, these effects
made thermal inside the building material reduced and
made it cooler.
surface temperature of vegetated façade also increased.
The effective thermal resistance of a plant layer gradually decreased when the air temperature rose. In other
hand, when the wind speed increased, the reduction in
interior façade surface temperature between bare and
vegetated façade also increased. These effects made
thermal inside the building material reduced and made
it cooler. It can be concluded that performance of
vertical garden influenced by the weather around the
building.
6 ACKNOWLEDGEMENT
5 CONCLUSION
We gratefully thank PT Pertamina Indonesia, branch
office Semarang for giving the licence to conduct this
In this research, a field measurement performed the research using vertical garden application in its buildpurpose of studies about performance of thermal re- ing.
duction of surface temperature in the interior building
façade by applying vertical garden.
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ing rooftop and vertical gardens as an adaptation
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stitute for Research and Construction, National
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Arsitektur Ekologis. Kanisius, Yogyakarta, Indonety confirm that the area of façade covered with vertical
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sia.
façade. When the outdoor air temperature increased,
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G. M. (1988). “Impacts of vegetation on residential heating and cooling.” Energy and Buildings,
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Widiastuti, R., P. E. B. W. (2014). “Kenyamanan termal bangunan dengan vertical garden berdasarkan standar kenyamanan mom &
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Vol 5, No 1, March 2016, 21-28
Modeling Infrastructure Bridges Maintenance Work Zones
Mohamed Marzouk∗ and Kouzal El Banna
Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt
Abstract: Costs of infrastructure bridges maintenance are attributed to the cost of maintenance and disruptive
delays to users. To achieve effectiveness in managing the assets of infrastructure systems, many interdisciplinary
tools and concepts are integrated and deployed to achieve lifecycle cost optimization in an effort to achieve
sustainable infrastructures. As bridges become older and maintenance costs become higher, governmental
authorities responsible for bridges maintenance face challenges with respect to implementation of optimal bridge
management programs based on life-cycle cost considerations. This paper presents a model that determines
the costs associated with each item of bridges maintenance. The model takes into consideration work zone user
costs. It also compares between data in deterministic condition and probabilistic condition using simulation
optimization. A comprehensive case study of El-Giza bridge maintenance is presented to demonstrate the
practical features of the proposed model.
Keywords: Infrastructure bridges, life cycle cost analysis, maintenance costs, optimization
DOI: 10.7492/IJAEC.2016.003
1 INTRODUCTION
Bridges represent a substantial investment of public
funds, and are expected to provide satisfactory performance and remain in service for many years. For new
bridges, design specifications typically require 75- or
100- year design life. Bridges deteriorate over time due
to several factors including weather (Zhu et al. 2007;
Mondal and DeWolf 2007), traffic volume, poor design
work, poor quality of construction (Belli et al. 2008).
Table (1) lists the factors that influence bridges deterioration as reported by Huang et al. (2010) based on
literature (Jiang 1990; Scherer and Glagola 1994; Zhao
and Chen 2002; Su 2003). Moreover, even bridges not
suffering from any serious deterioration may become
obsolete with time because of increases in legal load
standards and modifications of bridge design codes.
Consequently, as the age of existing bridges increases,
more resources need to be allocated for their maintenance, rehabilitation, and replacement (ARMY TM 5600/AFJPAM 32-1088 1994). Several research efforts
have been made to diagnose bridges’ deterioration using Markov-chain (Scherer and Glagola 1994), fuzzy
system (Zhao and Chen 2002), logistic regression analysis (Su 2003). It is worth noting that before conducting any action towards existing bridges deteriorations
careful analyses such as an understanding of the symp-
toms and the causative problems are essential in the
condition assessment of bridge structures. This can be
done by site investigations and laboratory tests. Subsequently, life cycle cost analysis is carried out in order
to select the most efficient solution for treatment of the
bridge.
Planning for asset management should take into consideration the overall life cycle costs of providing the
service and be prepared to make investment decisions
accordingly. Asset management involves several aspects (InfraGuide 2005), including asset value, life cycle management, long-term affordability, risk management and assessment, performance measurement, operational plans, and integration of technical and financial plans. The framework for an asset management
plan can be described in terms of seven questions (InfraGuide 2005):
21
1. What do you have and where is it? (Inventory)
2. What is it worth? (Costs/replacement rates)
3. What is its condition and expected remaining service life? (Condition and capability analysis)
4. What is the level of service expectation, and what
needs to be done? (Capital and operating plans)
5. When do you need to do it? (Capital and operating plans)
6. How much will it cost and what is the acceptable
Marzouk and Banna/International Journal of Architecture, Engineering and Construction 5 (2016) 21-28
Table 1. Bridge deterioration factors
Criteria
General Factors
Structural factors
Traffic factors
Environmental factors
Others
Factors
Bridge age, No. of spans, No. of lanes, Length of bridge, Area/width of deck, Max. span, Skew angle
Structural type, Girder type, Girder material, Abutment type, Pavement, Earthquake bracing,
Expansion joint, Wing wall, Designed live load
Traffic volume
Over water or not , Distance from coast, Acid rain, Average yearly rainfall, Average rainy days
per year, Soil profile
Road level, Climate region
level of risk(s)? (Short- and long-term financial
Work zones often cause traffic congestion on high volplan)
ume roads. As traffic volumes increase so does work
7. How do you ensure long-term affordability? zone-related traffic congestion and so does the public demand for road agencies to decrease both their
(Short- and long-term financial plan)
number and duration. Negative impacts on road users
It is recommended that inspections be made annual- can be minimized by bundling interventions on sevly of all basic structures and more frequently for fend- eral interconnected road sections instead of treating
ers and utilities. Additional inspections may be neces- each road section separately. Negative impacts on road
sary under certain circumstances, such as a tsunami, users can be quantified in user costs. The optimum
earthquakes, and accidents. Bridges can be inspect- work zone is the one that results in the minimum overed following one of the following types (ARMY TM all agency and user costs. The minimization of these
5-600/AFJPAM 32-1088 1994):
costs is often the goal of corridor planning. In order to
achieve this goal the interventions on each asset type
1. Operator inspection: it consists of examination,
(pavement, bridges, tunnels, hardware, etc.) must be
lubrication, and minor adjustment performed by
bundled into optimum packages. Hajdin and Lindenoperators on a continuous basis.
mann (2007) presented a method that enables road
2. Preventative maintenance inspection: is the
agencies to determine optimum work zones and interscheduled examination and minor repair of favention packages. The method allows the consideration
cilities and systems that would otherwise not be
of both budget constraints and distance constraints,
subject to inspection (e.g., pier fender systems).
including maximum permissible work zone length or
3. Control inspection: is the major scheduled exam- minimum distance between work zones. The matheination of all components and systems on a pe- matical formulation of this optimization problem is a
riodic basis to determine and document the con- binary program that can be solved by existing techdition of the bridge and to generate major work niques (i.e., the branch-and-bound method).
required.
Pavements on two-lane two-way highways are usuRehabilitation of bridges impacts their users in differ- ally resurfaced by closing one lane at a time. Vehient aspects; inconvenience to local business and com- cles then travel in the remaining lane along the work
munity, noise and environmental impacts (Mallela and zone, alternating directions within each control cycle.
Sadavisam 2011). Work zone road user costs are used Several alternatives can be evaluated, defined by the
as economic basis for quantifying these adverse impacts number of closed lanes and fractions of traffic divertwhich can then be used for effective decision-making ed to alternate routes. Chen et al. (2005) presented
to improve work zone mobility and safety (Mallela and an algorithm, referred to as SAUASD (Simulated AnSadavisam 2011; Benekohal et al. 2010). This paper nealing for Uniform Alternatives with a Single Detour),
presents a model that utilizes simulation optimization to find the best single alternative within a resurfacing
to analyze life cycle costs of bridges. The model adop- project. SAMASD is developed to search through posts metaheuristic optimization as an iterative generation sible mixed alternatives and their diverted fractions,
process to explore and exploit the search space in an to minimize total cost, further including agency cost
effort to reach near optimum solutions. Metaheuristic (resurfacing cost and idling cost) and user cost (user deoptimization combine basic heuristic methods in high- lay cost and accident cost). Thus, traffic management
er level frameworks aimed at efficiently and effectively plans are developed with uniform or mixed alternatives
exploring a search space (Blum and Roli 2003). The within a two-lane highway resurfacing project. Several
model has several features: i) it determines the costs research efforts have been made in highway mainteassociated to each item of bridge maintenance, ii) it nance and lane closures (Wang et al. 2002; Lee 2009;
calculates bridge maintenance costs over the service Meng and Weng 2010; Yang et al. 2009; Jiang et al.
life of the bridge by determining the NPV for these 2009; Christodoulou et al. 2012). This paper presents
costs, iii) it considers work zone user costs, and iv) it a framework that is dedicated for determining the opcompares deterministic condition data against proba- timum length of highway resurfacing work zone with
bilistic condition data using simulation optimization. minimum cost. A numerical example is worked out
A numerical example is presented to demonstrate the to demonstrate the essential features of the proposed
practical features of the proposed model.
framework.
22
2 LIFE CYCLE COST ANALYSIS
The life cycle cost (LCC) of an asset is defined as the
total cost, in present value or annual value that includes the initial costs, maintenance, repair and renewal (MR&R) costs over the service life or a specified
life cycle, whereas, life cycle cost analysis (LCCA) is a
process for evaluating the total economic worth of a usable project investment by analyzing initial costs and
discounted future costs, such as maintenance, use, reconstruction, rehabilitation, restoring, resurfacing, and
disposal costs, over the life of the project segment
(Rahman and Vanier 2004). LCCA is to estimate the
overall costs of treatment methods or options and select the best one that ensures the facility will provide
the lowest overall cost of ownership consistent with its
quality and function (Humphreys et al. 2007). A probabilistic life cycle costing analysis can be used to obtain
a more realistic assessment of the benefits of innovative
materials and technologies, whilst giving asset manager a basis to arrive at an acceptable level of risk, taking
into account the reliability of proven/traditional solutions weighed against innovative solutions (Humphreys
et al. 2007). One technique that has been used to account for the inherent uncertainty that is being widely promoted for incorporation in the evaluation of infrastructure projects specifically in (LCCA) is Monte
Carlo Simulation. This technique randomly samples
values for the uncertain input parameters according
to their pre-constructed probability distributions and
records the responses from the model, in the case of
the (LCCA) model, for the sampled values. This process is iterated numerous times until the preset convergence criteria are met, after which the recorded system
responses are used to construct the probability distribution of the outcome, the NPV as Equation (1).
s III and Smith 1998). Each work zone is associated
with a different user costs. As such, each work zone
should be evaluated separately when characteristics of
the work zone or the characteristics of the affected traffic change. Bridge rehabilitation and maintenance activities generally occur at different points in the analysis period with different traffic, and they generally vary
in scope and duration. The time that they occur also
affects the influence of the discount factor used in developing NPV (Walls III and Smith 1998). Schonfeld
and Chien (1999) developed a work zone cost function
which includes user delay cost and maintenance cost as
per Equation (2).
CT = CM + CU
(2)
Where; CT is total cost per lane-kilometer; CM is
maintenance cost per lane-kilometer; and CU is user
delay cost per lane-kilometer.
The user delay cost consists of the queuing delay
costs through work zones. Zone delay cost without any
alternate route around the work zone and is calculated
based on Equation (3).
Cq =
3600
(Z3 + Z4 L)[Q1 ( 3600
H − Q1 ) + Q2 ( H − Q2 )]v
V ( 3600
H − Q1 − Q2 )
(3)
Where; Cq is queuing delay cost per lane-kilometer;
Z3 is setup time; Z4 is average maintenance time per
lane-kilometer; L is work zone length; Q1 is hourly flow
rate in Direction 1; Q2 is hourly flow rate in Direction
2; H is average headway; V is average work zone speed;
v is value of user time; and Z3 + Z4 L represents the
total maintenance duration per zone. Equation 3 represents the queuing delay cost due to one-way traffic
control, the moving delay cost of the traffic flow Q1
and Q2 , denoted as Cv is the cost increment due to
n
the work zone. It is calculated based on Equation (4)
X
Ct
after considering the following factors (Marzouk et al.
(
1)
NPV =
(1 + i)t
t=0
2011):
Where; N P V = Net Present Value of life cycle costs,
1. The average maintenance duration per kilometer
Ct = sum of all relevant costs occurring in year t, n =
Z3
L + Z4
length of analyzed period, and i = discount rate.
2. The travel time difference over zone length with
the work, VL , and without the work zone, VL0 , and
3 WORK ZONE USER COSTS
3. The value of time, v, thus:
Z3
L
L
Work zone user costs are the increased vehicle operCv = (Q1 + Q2 )(
+ Z4 )( − )v
(4)
L
V
V0
ating cost, delay, and crash costs to highway users
resulting from construction, maintenance, or rehabilitation work zones. These costs are function of the
timing, duration, frequency, scope, and characteristics
of the work zone; the volume and operating characteristics of the traffic affected; and the dollar cost rates
assigned to vehicle operating, delay, and crashes. Work
Zone is defined as an area of a highway where maintenance and construction operations impact the number of lanes available to traffic or affect the operational
characteristics of traffic flowing through the area (Wall-
Where; V0 represents the speed on the original road
without any work zone. The user delay cost for this
solution CU is equal to the sum of queue delay cost Cq
and moving delay cost Cv as per Equation (5):
CU = Cq + Cv
(5)
The accident cost incurred by the traffic passing the
work zone can be determined from the number of accidents per 100 million vehicle hours multiplied by the
C
product of the increasing delay ( vq + Cvv ) and the aver-
23
age cost per accident va . As such, the average accident in longer zones. Since work zones lengths and maincost per lane-kilometer Ca is formulated as per Equa- tenance duration affect maintenance and user cost, it
tion (6) (Fouad 2011):
is important to determine the tradeoff between main3600
3600
tenance cost and user cost in order to minimize total
(Z +Z L)[Q1 ( H −Q1 )+Q2 ( H −Q2 )]v
]na va
[ 3 4
V ( 3600
−Q
−Q
)
cost (Marzouk and Fouad 2014; Fouad 2011).
1
2
H
(6)
Ca =
Maintenance cost usually includes labor cost, equip108
ment
cost, material cost and traffic management cost.
The maintenance cost per zone is assumed to be
The
first
step in estimating maintenance cost is to deZ1 + Z2 L, where Z1 is fixed setup cost; and Z2 is avtermine
construction
quantities/unit prices. In this
erage maintenance cost per additional lane-kilometer.
research,
the
cost
of
maintaining
cost of length L is
The average maintenance cost per lane-kilometer, CM ,
assumed
to
be
a
linear
function,
of
the form CM =
is the total maintenance cost per zone divided by the
Z
+
Z
L,
in
which
Z
represents
the
fixed
cost for set1
2
1
zone length L as per Equation (7). Then, the total cost
ting
up
a
work
zone
and
Z
is
the
average
additional
2
for this solution as Equation (8):
maintenance cost per work zone unit length. The comZ1 + Z2 L
Z1
CM =
=
+ Z2
(7) ponents of user cost user delay cost and accident cost.
L
L
The user delay can be classified into queuing delay and
moving delay. The user delay cost is determined by
CT = CM + CU + Ca
(8)
multiplying the user delay by the value of user time
The developed simulation module captures the se- (Marzouk et al. 2011).The accident cost is related to
quence of tasks involved in the resurfacing operation the historical accident rate, delay, work zone configuraand the relationships between these tasks. The proce- tion, and average cost per accident. Optimization varidure of designing and building a simulation model can ables are any entities within studied system, where any
change in this entity would seriously affect the observed
be summarized as following:
optimization functions. Based on interviews with ex1. Break-down the operation into main processes pert engineers and extensive analysis of resurfacing opand tasks. For each task, type of resources (i.e., eration, optimization variables have been determined.
materials, labor, and/or equipment) involved in The considered optimization variables are:
its execution is identified.
1. Hourly Flow Rate in Direction 1 (Q1 ): Number
2. Indicated each type of tasks, either: Normal or
of vehicle in the same direction with work zone.
Combi depending on its need of resources.
2. Hourly Flow Rate in Direction 2 (Q2 ): Number
3. Representing the sequence and relationships beof vehicle in opposite direction against work zone.
tween tasks by using Arcs to map the network.
3.
Average maintenance time per lane-kilometer
4. Add more control logical conditions by created
(Z4 ): the required duration for maintenance for
control statements, which cannot be modeled useach lane per kilometer.
ing normal arcs and tasks.
4.
Work zone length (L): the optimum length for
5. Using simulation language to code the simulation
work
zone that decreases delay in traffic time and
network and control statements.
decrease
accidents.
6. Verify the simulation model and test it.
5. Average work zone speed (V ): speed of vehicle at
work zone.
6.
Average headway (H): the time of the distance
4 OPTIMIZING WORK ZONE USERS
between
two vehicles.
COSTS
The objective of the work zone optimization problem
is to minimize the total cost for work zone activities.
The objective function for work zone activities can be
expressed as per Equation (9):
5 NUMERICAL EXAMPLE
In order to demonstrate the use of the proposed simulation optimization model in optimizing bridges rehabilM in CT = CM + CU + Ca
(9)
itation, an actual project example is considered of ElWhere; CT is total cost, CM is maintenance cost, and Giza Bridge. The Bridge is considered the most imporCU is user cost.
tant bridges in El-Giza Governorate-Egypt. The bridge
The controllable variable affecting CM include work connects El-Harm, Faisl, and Munib streets to Cairo
zone length, fixed setup cost, and average maintenance University, Murad, and Abbas streets (see Figure 1).
cost per unit length; the controllable variables affect- The example considers maintenance of 1 Km length.
ing CU include work zone length, traffic volume, speed, Table 2 lists input values for the different considered
etc. Both CM and CU are function of work zone length. parameters. These values are either taken based on
It should be noted that longer zones tend to increase literature (Fouad 2011) (e.g., cost of user time value,
the users delays, but the maintenance activities can be number of accidents per 100 million vehicle hours, avperformed more efficiently with fewer repeated setups erage cost per accident, interest factor), feedback from
24
Table 2. Example input parameters
Parameters
v: cost of user time value
V0 : road speed at normal condition (without any work zone)
na : number of accidents per 100 million vehicle hours
va : average cost per accident
r: interest factor
I: inflation Index
Z1 : fixed cost for setting up a work zone
Z3 : standing time
Value
12.7 LE/Veh.hr
80 Km/hr
67 accident/100mvh
17.6 LE/hr
8%
5%
700,000 LE
10 hr/zone
Table 3. Bridge maintenance cost items
Cost Items
Unit
Expansion joints
Lm
Fence works
Lm
Brushes bridges metal flooring
m2
Paint metal sectors in bridge
m2
Maintaining bridge shoulders
Lm
Maintaining bridge supports
No.
Z2 (LE/Km) Total
Quantity
LE/Unit
30
250
2500
40000
350
16
5,800
750
485
78
350
1,250
Minimum
165,300
178,125
1,151,875
2,964,000
116,375
19,000
4,594,675
Z2 (LE/Km)
Most likely
174,000
187,500
1,212,500
3,120,000
122,500
20,000
4,836,500
Maximum
182,700
196,875
1,273,125
3,276,000
128,625
21,000
5,078,325
Table 4. Optimization parameters
Parameter
Q1
Q2
Z4
L (work zone length)
V
H
Range
3000-5000 Veh/hr
0 Veh/hr (one way bridge)
10-20 Hr/Lane.Km
0.1-1 Km
10-15 Km/hr
2-10 Sec
Table 5. Estimated net present value over bridge life
Year
10
20
30
40
50
60
70
Inflation rate
1.63
2.65
4.32
7.04
11.47
18.68
30.43
NPV Total
Interest rate
0.4632
0.2145
0.0994
0.046
0.0213
0.0099
0.0046
NPV (LE)
4,572,992
3,450,292
2,603,223
1,964,114
1,481,911
1,118,092
843,593
16,034,218
*Note: For year n, Inflation rate = (1 + I)n , Interest rate =
NPV = 6,061,010 ∗ Inflation rate ∗ Interest rate
construction practitioners (e.g., interest factor, inflation index, fixed cost for setting up a work zone, standing time), or actual data of the bridge (e.g., road speed
at normal condition). Table 3 lists Maintenance Cost
(CM ) and the average additional maintenance cost per
work zone unit length (Z2 ) for the different items of
bridge maintenance. Triangle distribution has been assumed for the average additional maintenance cost per
work zone unit length (Z2 ). Optimization parameters
are listed in Table 4. Applying the input parameters in
Equation (4), the moving delay cost (Cv ) is estimated
to be 60,008 LE/Lane.Km. Whereas, the queue delay
cost (Cq ) is estimated to be 71,120 LE/Lane.Km. By
1
(1+r)n
applying in the input parameters in Equation 6, the
value of the average accident cost (Ca ) is very minor
and it can be neglected. The bridge consists of four
lanes and it has one closure lane, also one kilometer
length, as such; the total cost is estimated as follows,
considering the values of most likely maintenance cost,
given in Table 3:
CT = CM + CU + Ca
= 4, 836, 500 + 700, 000 + 4 ∗ (60, 008 + 71, 120)
= 6, 061, 010LE.
The net present value (NPV) for the bridge over its
life is estimated using Table 5. Considering 70 years
and the maintenance takes place every 10 years, the
25
Figure 1. Layout of El-Giza bridge
Figure 2. Net present value simulation results
Figure 3. Maintenance optimum solution
26
total net present value is 16,034,218 LE. Subsequenting the deterioration factors of RC bridge decks: A
ly, 2000 simulation runs were executed. The NPV net
rough set approach.” Computer-Aided Civil and Inpresent value changes from 16,166,787 to 15,315,092.
frastructure Engineering, 25(7), 517–529.
For certainty level 95.00%, the NPV net present value Humphreys, M., Setunge, S., Fenwick, J., and Alwi, S.
changes from 14,830,198 to 15,536,178 (see Figure 2).
(2007). “Strategies for minimising the whole of life
The optimum solution is obtained at 15,315,092, concycle cost of reinforced concrete bridge exposed to
sidering the values of the parameters, given in Figure 3.
aggressive environments.” Proceedings of the Second
International Conference on Quality Chain Management, Stockholm.
InfraGuide (2005). Managing Infrastructure Assets.
6 SUMMARY
Federation of Canadian Municipalities (FCM), OtThe level of deterioration in bridges depends on many
tawa, Ontario, Canada.
factors including corrosion of reinforcing steel, condi- Jiang, Y. (1990). “The development of performance
tion of concrete and external environments. One of the
prediction and optimization models for bridge mancritical issues causing reduced service life of the bridge
agement systems.” Ph.D. thesis, Purdue University,
was a delay of conducting bridge maintenance. FurUnited States.
thermore, delaying bridge maintenance causes increase Jiang, Y., Chen, H., and Li, S. (2009). “Computation
in cost due to repair and rehabilitation. This paper preof user costs at freeway work zones using weigh-insented a model that is capable to determine the costs
motion traffic data.” International Journal of Conassociated with each item of bridges maintenance. The
struction Education and Research, 5(3), 197–219.
model takes into consideration work zone user costs. A Lee, H. Y. (2009). “Optimizing schedule for improving
numerical example, of El-Giza Bridge, was presented to
the traffic impact of work zone on roads.” Automademonstrate the use of the proposed simulation optition in Construction, 18(8), 1034–1044.
mization model in optimizing bridges rehabilitation.
Mallela, J. and Sadavisam, S. (2011). “Work zone road
user costs concepts and applications.” Report No.
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28
Vol 5, No 1, March 2016, 29-43
Organizational Competencies and Project Performance
c
): Evaluating Construction Project
Tool (OCPPT
Competencies and Performance
Moataz Nabil Omar1 and Aminah Robinson Fayek2,∗
1
Hole School of Construction Engineering, Department of Civil and Environmental Engineering
University of Alberta, Edmonton, Canada
2
NSERC Industrial Research Chair in Strategic Construction Modeling and Delivery
Ledcor Professor in Construction Engineering, Hole School of Construction Engineering
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Canada
Abstract: Construction projects are completed in a constantly changing environment as a result of merging
many interactions, known as project competencies, with varying processes. Many project competencies can be
quantified and then used to differentiate superior from average performance. Construction organizations lack
a tool that is capable of evaluating project competencies and performance measures, which would allow them
to capture and anticipate continuous changes after project execution. In this paper, an overview of previous
research regarding organizational competencies and project performance is presented to illustrate the need in
the construction domain for a resource with these capabilities. To remedy these limitations, the Organizational
c
c
has a user interface
) is presented. The OCPPT
Competencies and Project Performance Tool (OCPPT
and database to evaluate project competencies and project key performance indicators, which will provide
construction organizations with a means to better quantify trends of improvement throughout the life cycle of
construction projects.
Keywords: Organizational performance, prediction models, organizational functional units, regression
modeling
DOI: 10.7492/IJAEC.2016.004
1 INTRODUCTION
In previous research, project competencies have occasionally been considered as measures of project performance (Fayek 2012); as a result, this research did
In contemporary construction environments, companot investigate project competencies as prerequisites
nies measure their performance against a set of predefor project performance, nor did it explore the fact
fined performance indicators. These performance indithat project competencies are leading indicators for
cators are governed by the ability of the company to
project performance improvement. Researchers conmaintain necessary sets of “competencies” that empowcluded that defining, measuring, and evaluating the
er the successful execution of construction projects. In
different project competencies as leading indicators to
general, competencies are difficult to define and meaproject performance will result in better project perforsure due to the subjective nature of their assessment.
mance (Sparrow 1995; Walsh and Linton 2001; Markus
Performance indicators, on the other hand, are lagging
et al. 2005). In this paper, previous studies are first
indicators that capture the different critical aspects of
identified and discussed to offer background on the curhow well a construction project is performing. Evalrent state of the art in the areas of project competencies
uating the different project competencies and project
and project performance. Next, a detailed illustration
performance measures will allow for the identification
c
of the OCPPT
is presented. Finally, the setup and
of project competencies that require further improvec
evaluation modules of the OCPPT
are presented alment, which will, in turn, result in improved project
ong with an illustrative case study to highlight the asperformance (Antonacopoulou and FitzGerald 1996).
29
Omar and Fayek/International Journal of Architecture, Engineering and Construction 5 (2016) 29-43
sessment process of project competencies and project
performance.
2 ORGANIZATIONAL
COMPETENCIES AND PROJECT
PERFORMANCE EVALUATION
MODELS
A distinction between project performance and project
competencies as a prerequisite for project performance
has not been established in previous research (Fayek
2012). Moreover, few studies have provided a distinction between project competencies and project performance (Fayek 2012; Omar and Fayek 2014; Omar and
Fayek 2016a).
In 1986, Hitt and Ireland used corporate level competencies to evaluate performance. Lyle and Signe
(1993) developed an “iceberg model” that considers the
qualities of individuals as one element of the model
and knowledge and skills as the second element; the
two elements are used jointly for evaluating performance. Spencer and Spencer concluded that in order to adequately measure competencies, the personal and professional competencies of individuals in an
organization must be considered. Sparrow (1995) attempted to integrate the different concepts of organizational competencies described in previous research
through the different levels of an organization and presented three main approaches to evaluating organizational competencies: 1) the “management competence”
approach was introduced for the purpose of evaluating effectiveness across different occupations and sectors within an organization; 2) the “behavioral competence” approach, which complements the “management approach”, was introduced to evaluate individuals across different occupations and management hierarchies within an organization; and 3) the “core competence” approach was used to identify the resources and
capabilities of the organization that are connected to
overall performance. Sparrow concluded that looking
for ways to reintegrate the aforementioned three approaches in organizations will enhance organizational
performance.
Walsh and Linton (2001) first made the distinction
between competencies and capabilities. When evaluating performance, Walsh and Linton limited their
investigation to core competencies. Competencies
were defined as “firm specific technologies and production related skills”, while capabilities were defined
as “firm specific business practices, processes and culture” (Walsh and Linton 2001). According to Walsh
and Linton, the implementation of the two concepts
requires a deep understanding of what core competencies are. Core competencies are a “relative pursuit”
where companies and project groups tend to gauge
their competencies in terms of benchmarking. Accordingly, competencies are being assessed to achieve supe-
rior performance. Isik et al. (2009) applied a structural equation model to establish the relationship between different management competencies and organizational strengths and weaknesses. Alroomi et al.
(2011) proposed a core-competency estimation framework and methodology to prioritize cost-estimator behavioral competencies on the basis of the combined effects of the level of importance of each competency and
the associated gap between the ideal and actual level of
competency. A correlation analysis was conducted to
measure the degree of relationship between the different behavioral competencies. Factor analysis was then
used to group the predefined behavioral competencies
into factor groups.
According to previous research, two main categories
of project competencies have been identified (Omar
and Fayek 2014, 2016a). The first category is attributable to how an organization functions, while the
second category is attributable to the competencies attained by individuals. Together, the two categories
contribute to better construction project performance.
Lists of functional and behavioral competencies are
provided in Table 1 and 2 (Omar and Fayek 2016a),
respectively.
Each of the project competencies is further divided
into a set of evaluation criteria that are evaluated using predefined scales; functional competencies consist
of 162 evaluation criteria and behavioral competencies
consist of 86 evaluation criteria. To capture the subjectivity and uncertainty associated with the functional competencies’ evaluation criteria, two scales may be
used for measuring them. The first scale is the maturity scale (Sarshar et al. 2000; Willis and Rankin
2010, 2012; Omar and Fayek 2016a), which measures
the relevance of the different evaluation criteria to a
construction project and to what degree the different
evaluation criteria are implemented. The second scale
is the importance scale, which is a five-point scale ranging from 1 “extremely unimportant” to 5 “extremely
important” (Omar and Fayek 2016a). The importance
scale is used to measure and prioritize the evaluation
criteria pertaining to each functional competency.
Two scales may be used for measuring the different behavioral competencies. A seven-point bipolar
linguistic agreement scale (Ajzen 1991), ranging from
a negative evaluation (e.g., strongly disagree) on one
end to a positive evaluation on the other end (e.g.,
strongly agree), is used to form a bipolar continuum for
evaluating subjective and uncertain human behaviours
such as behavioral competencies. Similar to the functional competencies’ evaluation criteria, the second
scale considered for measuring behavioral competencies is the importance scale (Omar and Fayek 2016a).
The different scales for measuring project competencies
(i.e., functional and behavioral competencies) are presented in Figure 1 (Omar and Fayek 2016a).
As for project performance, in the early 1990s, the
evaluation of the success of construction projects was
30
Table 1. Functional competencies
1. Project Integration Management
2. Project Scope Management
3. Project Time Management
4. Project Cost Management
5. Project Engineering and Procurement Management
6. Project Resource Management
7. Project Risk Management
8. Project Communication Management
9. Project Safety Management
10. Project Human Resource Management
11. Project Quality Management
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
Project
Project
Project
Project
Project
Project
Project
Project
Project
Project
Change Management
Stakeholders Management
Environmental Management
Commissioning and Startup
Innovation
Workface Planning
Contract Administration
Team Building
Workforce Development
Technology Integration
Table 2. Behavioral competencies
1. Analytical Ability
2. Training
3. Assessment Ability
4. Decision Making
5. Leadership
6. Teamwork
7. Consultation
8. Motivation
9. Negotiation and Crisis Resolution
10. Ethics
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
Self-Control
Reliability
Problem Solving
Commitment
Adaptability
Building Trust
Interpersonal Skills
Influence (Assertiveness)
Cultural Competence
Initiative
Figure 1. Project competencies’ measurement scales
tied to a few performance measures, which, in turn,
were tied to the projects’ objectives (Kaplan and Norton 2005; Eccles 1991; Bourne et al. 2000; Norreklit 2000; Bassioni et al. 2004). These performance
measures were a function of project duration, cost,
and quality (Navarre and Schaan 1990). Recent studies have focused on evaluating project performance
through best practices and benchmarking programs.
Construction best practices developed in the UK introduced the project KPIs measurement program, which
defined sets of project KPIs for different project and
organizational levels that directly reflect the current performance and performance targets for organiza-
tions and projects (Egan 1998). Similarly, the Canadian Construction Innovation Council (CCIC), the Construction Industry Institute (CII), and Construction
Owner Association of Alberta (COAA) have each developed a benchmarking program that facilitates data
collection and analysis pertaining to performance measures on projects ((Rankin et al. 2008; Nasir et al.
2012; CII and COAA 2009; CII 2013).
After reviewing the different frameworks and identifying the advantages of each, an updated framework
and a detailed set of performance metrics and project
KPIs were developed by Omar and Fayek (2016a). The
categorization of performance measures into perfor-
31
mance metrics and sets of project KPIs provided a comprehensive overview of project performance through
seven different performance metrics consisting of 46
project KPIs. Table 3 lists the seven project performance categories and a sample of project KPIs.
Omar and Fayek 2014, 2016a identified a number of
project competencies and their evaluation criteria as
well as project performance measures. In addition, an
advanced fuzzy hybrid model incorporating fuzzy set
theory and artificial neural networks was also developed by Omar and Fayek (2016a) to enable the evaluation of project competencies and performance and to
help identify the relationship between project competencies and project performance. Findings from Omar
and Fayek (2016a) indicated a need for a tool for use
within the construction domain that would allow for
the evaluation of project competencies and project performance prior to modeling the relationship between
c
them. The OCPPT
, presented in this paper, was
developed to account for the assessment of the different project competencies and project performance
measures (i.e., project KPIs) thus allowing construc-
tion practitioners to store information related to their
project competencies and assess the performance of
their construction projects.
c
3 OCPPT
c
c
The OCPPT
was created using Visual Basic.net
c
and SQL to evaluate project competencies and
c
project performance. The OCPPT
allow users (i.e.,
researchers and construction practitioners) to analyze
different project competencies (i.e., functional and behavioral competencies) using project KPIs and the predetermined scales described earlier in this paper. This
evaluation was conducted prior to the application of
the fuzzy hybrid model developed by Omar and Fayek
(2016a).
c
The OCPPT
structure consists of two modules:
c
c
(1)OCPPT setup and (2)OCPPT
evaluation. Each
module has three sub-modules: (1)organizational and
projects’ structures, (2)project competencies, and
(3)project KPIs. The sub-modules account for the development of the different requirements for evaluating
Table 3. Examples of performance measures (Omar and Fayek 2016a)
Performance
metric
Cost
KPI number
1.1
KPI
description
Project Cost Growth
Schedule
2.1
Project
Growth
Change
3.1
Total Change Cost
Factor
Safety
4.1
Lost Time Rate
Quality
5.1
Total Field Rework
Cost Factor
Productivity
6.1
Construction
Productivity Factor
(Cost)
Satisfaction
7.1
Satisfaction (Design
team)
Schedule
KPI definition
KPI formula
The variance between the
actual total project cost todate and the total project
estimate to-date at tender
stage, expressed as a ratio
of the total project estimate
to-date at tender stage
The variance between the
actual total project duration to-date and the project
duration to-date at tender
stage, expressed as a ratio of
the project duration to-date
at tender stage
The ratio between the total
cost of scope changes (contractor and client) to-date
and the actual total project
cost to-date
The ratio between the time
lost to incidents in hours
measured over 100,000 hours
of work
The ratio between the total direct cost of field rework
to-date, and the actual construction phase cost to-date
The ratio between the total
installed work cost to-date
and the total actual manhours to-date
Owner/Contractor overall
satisfaction with the design
team
(Actual total project cost
– total project estimate at
tender stage) / total project
estimate at tender stage
32
(Actual total project duration – project duration at
tender stage) / project duration at tender stage
Total cost of scope changes
/ actual total project cost
Amount of lost time to incidents (in hours) / 100,000
hours of work
Total direct cost of field rework / actual construction
phase cost
Total installed cost / total
actual man-hours worked
Rating from 1 to 7, where, 1
is extremely dissatisfied and
7 is extremely satisfied
c
Figure 2. OCPPT
structure
construction projects in terms of project competencies
and project performance, respectively. A description
of each of the modules and sub-modules is presented
in Figure 2.
tional competencies that consist of 162 evaluation criteria and 20 behavioral competencies that consist of
86 evaluation criteria). The predefined libraries (i.e.,
functional and behavioral competencies) can be reconfigured to add, remove, and edit predefined project
c
competencies and criteria to suit each company’s nee4 OCPPT
SETUP MODULE
ds. Figure 5 displays the predefined functional compe4.1 Organizational and Projects’ Break- tencies’ library and a sample evaluation criteria pertaining to one of the functional competencies (i.e.,
down Structure Setup Sub-Module
project time management).
First, the organizational and projects’ breakdown
c
structure are defined in the OCPPT
. Project-specific
data are entered, as shown in Figure 3, to provide infor4.3 Project KPIs’ Setup Sub-Module
mation regarding project characteristics and progress.
Examples of project information include the following:
project name, contract type, project start date, project As for project KPIs, the OCPPT
c
has a predefined
value, and required project respondents for complet- library of project performance categories and project
ing the functional and behavioral competencies’ evalu- KPIs (i.e., seven performance categories that consist
ations.
of 46 project KPIs), as identified by Omar and Fayek
c
The OCPPT
is capable of including several organi- (2016a). The predefined library can be reconfigured to
zations (e.g., company A and company B) and projects add, remove, and edit predefined project KPIs to suit
within each organization when creating the organiza- each company’s needs. The predefined project perfortional and projects’ breakdown structure. First, orga- mance categories and a sample project KPIs library
nizations are created and then projects for each orga- are shown in Figure 6. A sample project KPI formunization are introduced, as presented in Figure 4.
la (i.e., Project Cost Growth) and variables created in
4.2 Project
Module
Competencies’
Setup
Sub-
After creating the organizational and projects’ breakdown structure, the user defines the different project
c
competencies to be evaluated. The OCPPT
has predefined libraries of project competencies (i.e., functional and behavioral competencies). The predefined libraries consist of project competencies’ evaluation criteria identified by Omar and Fayek (2016a) (21 func-
c
the OCPPT
are shown in Figure 7.
As displayed in Figure 6, variables for Project Cost
Growth KPIs (shown in the cost performance indicators category) represent the following: (1) actual total project cost to-date and (2) total project estimate
at tender stage to-date. The formula for calculating
Project Cost Growth KPIs is (actual total project cost
to-date - total project estimate at tender stage todate)/total project estimate at tender stage to-date.
33
Figure 3. Project information setup
Figure 4. Sample organizational and projects’ breakdown structure
Figure 5. Functional competencies library and sample evaluation criteria
34
Figure 6. Project performance categories and sample project KPIs
Figure 7. Project KPIs’ library
c
5 OCPPT
EVALUATION MODULE
liverables compared to its planned objectives.
The organization (i.e., company) and its projects, conc
sidered for evaluation and defined in the OCPPT
setup phase, are used to evaluate the different project
competencies. First, project respondents are identified
for assessing the evaluation criteria pertaining to the
different project competencies. Then, the evaluation
criteria are combined, using a prioritized aggregation
algorithm (Omar and Fayek 2016b), to produce overall evaluations representing the project competencies.
Similarly, project KPIs’ variables are entered to calculate the different project KPIs and to generate values
that measure how well the project is producing its de-
5.1 Organizational and Projects’ Breakdown Structure Evaluation SubModule
Company and project-specific information that are dec
fined in the OCPPT
setup module are used to identify project respondents who will evaluate the project
competencies. A sample project organizational breakdown structure used to identify respondents who are
participating in assessing the different project competencies (i.e., functional and behavioral competencies)
is presented in Figure 8.
35
values are then used to calculate the different project
KPIs. Figure 12 displays sample KPIs’ variables enc
tered in the OCPPT
to calculate the project KPIs.
The calculated evaluations of project competencies
and KPIs allow construction practitioners to measure
and evaluate the impact of project competencies on
performance. Furthermore, trends of improvement
can be detected by performing periodic evaluations of
project competencies and KPIs throughout the life cycle of the project. In the next section, an illustrative
case study highlighting the evaluative capabilities of
c
the OCPPT
is presented.
6 ILLUSTRATIVE CASE STUDY
Figure 8. Sample project organizational breakdown
structure
5.2 Project Competencies’ Evaluation SubModule
The different functional and behavioral competencies’
evaluation criteria are then assessed by the identified
respondents for each project. Figure 9 shows a sample
functional competency evaluation criteria entered into
c
.
the OCPPT
The assessed project competencies’ evaluation criteria for each project competency are then combined
using a prioritized aggregation algorithm (Omar and
Fayek 2016b). The prioritized aggregation algorithm
provides a collective evaluation for the different project
competencies (i.e., at the project competency level
rather than the evaluation-criteria level). The prioritized aggregation assesses both the relative importance
of the project competencies’ evaluation criteria as well
as the individual criteria pertaining to a given competency. Hence, a high maturity score of a lower priority
evaluation criterion for a given functional competency
will not compensate for a low maturity score of a higher priority evaluation criterion for the same functional
competency. Similarly, a high agreement score of a
lower priority evaluation criterion for a given behavioral competency will not compensate for a low agreement score of a higher priority evaluation criterion
for the same behavioral competency. Figure 10 display
a sample export of functional competencies’ evaluation
c
criteria for a given project using the OCPPT
.
The final evaluations for a given project’s functional
competencies after performing the prioritized aggregation are displayed in Table 4 and Figure 11, respectively.
5.3 Project KPIs Evaluation Sub-Module
In this case study, a sample commercial project is used
to demonstrate the setup and evaluation capabilities
c
of the OCPPT
. In terms of project percentage completion at the time the evaluations were conducted,
engineering works were 100% complete, construction
works were 60% complete, and the overall engineering and construction works were 70% complete. The
project team consisted of one project manager, one
foreman, and one crew of three electrical tradespeople. The project manager completed the evaluation of
project functional competencies. As for the evaluations
of behavioral competencies, a total of five evaluations
were completed by the project manager, foreman, and
three electrical tradespeople and were then analyzed to
determine the different behavioral competencies of the
team. Out of the five evaluations, two were behavioral
competencies evaluations of supervisors (i.e., project
manager and foreman) and three were evaluations of
team members (i.e., electrical tradespeople). Project
KPIs’ data relevant to project performance provided
by the project manager were used to derive projectspecific KPIs to facilitate performance evaluation for
this particular project. Each phase of the project setup and evaluation setups explained earlier in this paper
are applied in the illustrative case study, as described
in the next section.
6.1 Project Setup
First, the project’s characteristics, general information,
and project organizational breakdown structure are developed, as shown in Figure 13.
Second, the predefined libraries for the project competencies (i.e., functional and behavioral competencies) and the project KPIs are used to generate the
different project competencies’ evaluation criteria and
project KPIs for respondents on the construction
project. For simplicity, only two project KPIs are considered in the evaluation of this illustrative case study.
As described earlier in the project KPIs’ setup submodule, the project KPIs are calculated using predefined project performance metrics and KPIs. First, all 6.2 Project Evaluation
c
project KPIs’ variables are entered into the OCPPT
The different project competencies (i.e., functional
(e.g., by a project controls manager); the variables’ competencies to be evaluated by the project manag36
Figure 9. Sample functional competencies evaluation
Figure 10. Sample exported functional competencies’ evaluations
Figure 11. Sample graphical evaluation of project functional competencies
37
Table 4. Sample project functional competencies overall fuzzy and crisp evaluation
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Functional Competency
Project Integration Management
Project Scope Management
Project Time Management
Project Cost Management
Project Engineering and Procurement Management
Project Resource Management
Project Risk Management
Project Communication Management
Project Safety Management
Project Human Resource Management
Project Quality Management
Project Change Management
Project Stakeholders Management
Project Environmental Management
Project Commissioning and Startup
Project Innovation
Project Workface Planning
Project Contract Administration
Project Team Building
Project Workforce Development
Project Technology Integration
Project Overall Maturity Value
0.333
0.3
0.333
0.36
0.279
0.24
0.338
0.3
0.347
0.279
0.35
0.3
0.301
0.378
0.357
0.269
0.281
0.308
0.35
0.297
0.366
Figure 12. Sample project KPIs’ variables
er and behavioral competencies to be evaluated by the
project manager, foreman, and three electrical tradespeople) are evaluated by the identified project respondents. Similar to project competencies, project KPIs
are generated and completed by the project manager,
as described in the next section. The functional competencies are evaluated by management staff who oversee the application of the different organizational practices on the project. Accordingly, in this project, the
functional competencies were evaluated by the project
manager, as shown in Figure 14.
A radar diagram is generated for the functional competencies’ evaluation, as presented in Figure 15.
For the behavioral competencies, supervisors (i.e.,
the project manager and foreman) and team members
(i.e., the electrical tradespeople) evaluate the project
team’s behavioral competencies. Figure 16 displays the
behavioral competencies’ evaluations completed by the
different respondents (i.e., supervisors and team memc
bers) entered into the OCPPT
.
The different behavioral competencies’ evaluation
criteria are assessed by the supervisors for their team,
as displayed in Figure 17. In addition, team members perform self-evaluations of their own team, as described in the next section.
38
form aggregation. A radar diagram is then generated
for the team’s behavioral competencies’ evaluation, as
presented in Figure 18.
For project KPIs’ variables, data are collected at the
same time project competencies’ evaluations are completed. Sample project KPIs’ variables are displayed
in Figure 12, and a sample project KPIs’ evaluation is
shown in Table 6.
c
The OCPPT
is then utilized to model the relationship between project competencies and project KPIs,
as described by Omar and Fayek (2016a), by using the
overall project competencies evaluation (i.e., Figure 15
& 18) and project KPIs evaluation (i.e., Table 6).
7 CONCLUSIONS AND FUTURE
WORK
Figure 13. Project general information and breakdown structure
First, the aforementioned respondents’ behavioral
competencies’ evaluations are collected and entered to
c
the OCPPT
. Then, a consistency check is performed
using Cronbach’s alpha coefficient (Cronbach 1951).
Prior to the assessment of behavioral competencies,
this check is used to measure the internal consistency
of the data collected (Cronbach 1951) from a supervisor and his or her randomly selected team members.
For this project, the Cronbach’s alpha coefficient is calculated using the supervisor evaluation (i.e., foreman)
and the team members’ self-evaluations (i.e., three electrical tradespeople) as displayed in Table 5.
The generated Cronbach’s alpha coefficient indicates
that the consistency of the foreman’s evaluation with
the self-evaluations of the electrical tradespeople was
considered to be of “excellent consistency” (George and
Mallery 2003). Therefore, the supervisor evaluation
(i.e., the foreman evaluating the crew) was considered
sufficiently representative for the purpose of the analysis. After ensuring the consistency of the evaluations,
c
the data are exported to an Excel
template to per-
c
The OCPPT
was developed to evaluate project competencies and project KPIs.
First, an overview of previous research in the areas
of project competencies and project performance was
presented, and the need for a tool capable of evaluating project competencies and project performance
was assessed. Next, the different components of the
c
OCPPT
were laid out. Then, an illustrative case
study was presented to demonstrate the evaluative cac
c
allows con. The OCPPT
pabilities of the OCPPT
struction practitioners to evaluate their project competencies and project performance (i.e., project KPIs),
respectively, at different points in the project life cycle. This evaluation quantifies trends of improvement
in project competencies and KPIs throughout the life
cycle of the projects.
Future work will explore the development of additional built-in capabilities to identify and quantify the
relationship between project competencies and project
KPIs using advanced hybrid modeling techniques described by Omar and Fayek (2016a). Applying advanced hybrid modeling capabilities to the developed
c
OCPPT
will enable additional analyses, such as factor analysis and granular fuzzy neural networks, as
presented by Omar and Fayek (2016a). These new capabilities will allow construction practitioners to evaluate their projects’ competencies and performance, in
addition to helping them predict project performance
using existing evaluations of project competencies.
Table 5. Cronbach’s alpha coefficient for crew behavioral competencies’ evaluation
Statistics for Respondents
Number of evaluation criteria considered for consistency check
Mean for respondents
Standard deviation for respondents
Variance for respondents
Sum of evaluation criteria’s variance
Cronbach’s alpha coefficient
39
Value
20
123
7.528
56.667
4.823
0.963
Figure 14. Sample project KPIs’ variables
Figure 15. Project functional competencies graphical evaluation
Figure 16. Behavioral competencies respondents’ evaluations
40
Figure 17. Sample behavioral competencies supervisor evaluations
Figure 18. Project behavioral competencies graphical evaluation
Table 6. Sample project KPIs’ evaluations
KPI name
KPI definition
KPI formula
Project
Cost
Growth
Variance between the actual
total project cost and the total project estimate at tender stage, expressed as a ratio of the total project estimate at tender stage, and is
expressed as a percentage
The ratio between the actual total project cost to date
and the sum of the total
project estimate at tender
stage and approved changes
(actual total
cost – total
estimate at
stage) / total
estimate at
stage
Project
Budget
Factor
project
project
tender
project
tender
actual total project
cost / (total project
estimate at tender
stage + approved
changes)
41
KPI
value
7.53%
KPI threshold
1.60%
<0 Desirable Value;
=0 Planned Value;
>0 Undesirable Value
<0 Desirable Value;
=0 Planned Value;
>0 Undesirable Value
8 ACKNOWLEDGMENTS
This research is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Industrial Research Chair in Strategic Construction Modeling and Delivery and the NSERC Discovery Grant
for Advancing Fuzzy Hybrid Techniques for Competency Modeling of Construction Organizations, both
held by Dr. Aminah Robinson Fayek. The authors
gratefully acknowledge the support and data provided
by the industry partners, construction companies, and
construction project participants who participated in
this research study. The effort exercised by Maria AlHussein, Stephen Arychuck, and Ramandeep Dhatt in
c
the course of developing the OCPPT
is also gratefully recognized.
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43
Vol 5, No 1, March 2016, 44-52
Investigating and Ranking Labor Productivity Factors in the
Egyptian Construction Industry
Michael Gerges∗ , Ograbe Ahiakwo, Remon Aziz, Georgios Kapogiannis,
Messaoud Saidani and Danah Saraireh
Civil and Structural Engineering, Coventry University, CV1 5FB, United Kingdom
Abstract: This article sets out to investigate and rank the factors that affect labor productivity in the Egyptian
construction industry. To achieve this, a quantitative research methodology is adopted and it entails the use of
structured survey questionnaires. The data obtained was analysed using the relative importance index. The results
from the analysis revealed ten major factors affecting labor productivity in the construction industry in Egypt. These
factors are: tools and equipment shortages; delay in material delivery on site; payment delay; undisciplined labor;
material shortage; rework; labor expensive and skills; low quality of raw material; waiting for equipment to arrive; and
on-site accident. Consequently, these findings would serve as a useful tool and a basis to make recommendations to
governmental and construction personnel regarding the productivity of labor in the Egyptian construction industry.
Keywords: Construction industry, Egypt, delay, labor productivity, relative importance index
DOI: 10.7492/IJAEC.2016.005
1 INTRODUCTION
The construction industry has changed significantly due
to the use of advanced tools, technology, management
skills, material, and heavy equipment. The importance
of the industry can be measured by how much the sector
adds to the country’s economy through its contribution
to the Gross Domestic Product (GDP) and the portion
it takes in any nation’s employment population (Sweis
et al. 2009). The industry has become more complex due
to new business demands, challenges, large numbers of
parties as clients, contractors, consultants, stakeholders,
shareholders, regulators, and others. Laborers are a very
important part of the construction phase of any project,
since they are the ones who are actually responsible for
building the project. All construction projects rely on the
productivity of equipment and workers to achieve good
results.
investment expected to reach US$21bn by 2017 (UKTI
2013). Being the largest country in the Middle East
with the 4th largest economy, the Egyptian construction
industry has been facing a range of difficulties since the
2011 revolution.
With the high rate of population increase at 1.7% per
annum (World Bank 2012), construction work in Egypt
is increasing rapidly to meet the needs of the growing
population through the expansion of portable water
systems, residential housing, hotels, sanitary drainage
facilities and various infrastructure project (Mack et al.
2009).
Most of the construction workers in Egypt come from
Upper Egypt (the southern part of the country). They
usually move to Cairo for high wages, regular work, a
more exciting life, lack of rural job opportunities, and
The loss of construction labor productivity can be most importantly it gives them the chance to remit cash
attributed to various factors, and understanding how in order to support family members at home in the village
much these factors affect labor productivity is crucial (Zohry 2002).
to improving project performance, increasing profit, and
overall project success.
The aim of this paper is to identify the factors
The construction industry in Egypt is a multibillion- contributing to the decline in laborers’ productivity in the
dollar industry and it contributes approximately 15%- Egyptian Construction Industry, and thus affect project
17% of the GDP (Gross Domestic Product), with an performance.
44
Gerges et al./International Journal of Architecture, Engineering and Construction 5 (2016) 44-52
2 RESEARCH METHODOLOGY
Thorough literature review of previous studies indicated
that four major factors contribute to labor productivity.
These factors include management factors (e.g.
planning, incentive programs, and competency of labor
supervision), human factors (e.g. labor experience, skill
age, and education), external factors (e.g. training
sessions, design changes, payment delays, and government
law), resource factors (e.g. poor site conditions, material
storage location, and violation of safety rules) and
miscellaneous factors (accidents during construction,
shortages of water and power supply (Jarkas and Bitar
2011).
This research is therefore based on a questionnaire
survey designed to gather all necessary information
aimed at: (1) Understanding productivity challenges to
construction laborers; (2) Understanding the Egyptian
Construction Industry; (3) Identifying factors that
impacts the productivity of laborers in Egyptian
Construction projects; and (4) Proposing strategic drivers
that will enhance labor productivity. However, it was
agreed that the type and style of questionnaire should
not use lengthy questions; it should not use confusing
questions that can be easily misunderstood, resulting in
a low participation rate. In addition, Arabic translation
was used to ensure the questionnaire was properly
understood clearly.
16,400 contractors registered with the EFCBC in 2014,
compared to 41,000 contractors in 2010 (El-Behary
2013). That huge drop was either a result of contractor
bankruptcy or change of career. All contractors are
divided into seven groups. These groups differ based on
the annual income, number of employees, projects size,
tool and equipment rented or owned, number of engineers,
and years of contractor experience.
The researcher has decided to target contractors within
the first three classes. The first class included 188
contractors; the second includes 276 contractors, and the
third 312 contractors. The three classes add up to a total
of 776 contractors. The researcher has used the formula
(El-Gohary and Aziz 2013) have used and cities as (Hogg
et al. 2010):
n=
m
1 + ( m−1
N )
(1)
n= sample size of limited population
m= sample size of unlimited population
N = available population
The only unknown in this equation is the value of m,
which can be calculated using the following equation.
Z 2 ∗ P ∗ (1 − P )
(2)
ε2
Z is the statistical value of the confidence level used i.e.
2.1 Pilot Study
2.575, 1.96 and 1.645 for 99%, 95% and 90% confidence
levels. Since P is unknown, Sincich et al. (2001) stated
A pilot study was conducted to validate and improve
that value of 0.50 should be used as sample size. ε is
the questionnaire. According to (Hertzog 2008) sample
the maximum error of the point estimate. Using 95%
size for pilot study can be considered as 5% of the
confidence i.e. 5% significance level, the unlimited sample
questionnaires distributed. A draft of the questionnaire
size of the population “m” is approximately calculated as
was given to 13 (5% of 258 distributed questionnaires)
following:
construction project managers in Egyptian construction
projects, who have more than 10 years of experience. The
1.962 ∗ 0.50 ∗ (1 − 0.50)
aim of the pilot study was:
m=
= 385
(3)
0.052
1. To test the questionnaire based on its format
For the total number of targeted contractor under first,
(layout)
second, third class in EFCBC, N = 776, the representative
2. To test the wording of questions
sample size was calculated as follow:
3. To validate the list of factors being surveyed
4. To test the measurement scale
385
5. To test the accuracy of the Arabic translation
n=
= 257.5
(4)
1 + ( 385−1
776 )
m=
The draft questionnaire was collected back from =258 contracting companies
respondents, and certain changes were made to the factors
Based on the following equation a total number of
list and to the questionnaire. It was then approved before
being circulated. The Factors were reduced from 53 to 41, 258 contracting companies in Egypt will be surveyed
Arabic grammar and spelling of the questionnaire was as a sample to represent a sample of a total of
776 contractors. The respondents vary from project
corrected, and the overall design was also improved.
managers, construction managers, supervisors, engineers,
architects, and consultants in their organisations.
2.2 Sample Size
The contractors will be the ones who are registered in
the Egyptian Federation of Construction and Building
Contractors (EFCBC). Being registered in the EFCBC
means that the contractor holds a license to work
legally (El-Behary 2013).
There are more than
2.3 Primary Data Analysis
For analysing the data, relative importance index
technique was used and is calculated using the following
formula:
45
Relative importance index(%)
=
5(n5) + 4(n4) + 3(n3) + 2(n2) + n1
∗ 100
5(n1 + n2 + n3 + n4 + n5)
error in understanding the questionnaire, and respondent
are busy so they fill the questionnaire quickly without
(5) reading it carefully.
The relative importance index was used to rank the
factors. Where n1, n2, n3, n4, and n5 are the total
number of respondent who selected “1” Strongly not
important, “2” Not Important, “3” Neutral, “4” Important,
“5” Very Important, the factors were ranked based on an
average of the experience of the professionals targeted.
The factors were ranked using the relative importance
index by Microsoft Excel spreadsheet. All the data was
inserted into a spreadsheet to rank the factors.
Table 2. Group of factors affecting labor productivity
Factor group
Human/Labor Factors
Material Factors
Management Factors
External Factors
Average relative
importance index
77.73%
77.62%
73.65%
73.10%
Rank
1
2
3
4
3.1 Human Labor Factors
3 RESULTS AND ANALYSIS
3.1.1 Undisciplined Labor
Two methods of ranking were used: (1) all ranked
factors and (2) group ranked factors. The factors were
categorised into four different groups (human/l factors,
management factors, external factors, material factors),
making a total of 41 factors. Following is a summary of
the questionnaire conducted for establishing the factors
affecting labor productivity in the Egyptian Construction
Industry:
From Table 3, “Undisciplined labor” was ranked 1st with
a relative importance index of 82.63% and ranked 4th in
overall ranking. This factor is mainly as a result of the
nature of the Egyptian laborers; they tend to be very
undisciplined by chatting away rather than carrying out
the work, they tend to go away for unscheduled breaks at
regular intervals.
Table 3. Factors related to the human/labor group
Total questionnaire sent = 258
Number of questionnaire received = 227
Type = Hard Copies
Time taken to collect data = 60 days
% of questionnaire received = 87.98%
Factors
Undisciplined labor
Labor experience and skill
Personal/family problems
Working 7 days a week without
rest
Absenteeism
Labor motivation
Arguments between workers
Physical fatigue
Labor age (old/young)
Communication
problems
between labor and supervisor
Labor motivation
Table 1. Number of respondents and their professions
Respondent
Engineers
Foremen
Site Supervisors
Construction Managers
Project Managers
Quantity Surveyors
Architects
Total
Number
98
33
32
27
18
12
7
227
Relative importance
index (%)
82.63%
81.96%
80.37%
Rank
1
2
3
79.47%
4
79.21%
77.62%
75.67%
74.47%
74.27%
5
6
7
8
9
71.58%
10
77.62%
6
3.1.2 Labor Experience and Skill
Similarly, “Labor experience and skill” was ranked 2nd in
the group and overall ranking 7th between 41 factors with
a relative importance index of 81.96%, this validated why
most of the laborers where undisciplined. This ranking
was further supported by Durdyev and Mbachu (2011)
where labor productivity was measured in New Zealand
and Enshassi et al. (2011) where labor productivity
was also measured in Gaza both research indicated that
the experience of laborers affects the work done on site.
Furthermore, Kalsum et al. (2010) also stated that
“laborers migrated to other countries after the breakdown
of the soviet union” for a better income. The same
circumstances are found in Egypt, the majority of the
experienced and skilled laborers have travelled to the
Gulf countries for a better income, after 2011 and 2013
revolution. As a result contractors have employed young
Figure 1. Experience of the respondents
aged and inexperienced construction laborers to carry out
The ranking of the groups are very close to each other, these jobs. This usually results poor quality work, cost
with all group factors have a relative importance index overruns as a result of rework and delays in the project
above 70% this might be due to bias in the questionnaire, schedule.
Table 1 shows the percentage of the professions
surveyed, out of the 227 questionnaires received. All
contractors are divided into seven groups.
46
3.1.3 Personal/Family Problems
Personal/Family problems was ranked 3rd in the group
with a relative importance index of 80.37% and an overall
ranking of 13th between the 41 factors. This result is
similar to the results obtained by Zakeri et al. (1996),
where personal/family problems where ranked 3rd out
of 10th factors affecting labor productivity in Iranian
construction projects. Zakeri et al. (1996) reported that
“most large and developed projects are located in remote
and less developed areas, with poor access and insufficient
facilities”. Whilst the majority of the laborers come from
rural areas there are not seeing their families for days,
plus the economy crises the country is facing, add more
pressure to the laborers meeting their families’ needs.
Although this factor “Personal/Family problems”
affecting labor productivity in Egypt is different
from the result obtained by Ailabouni (2007) where
“Personal/Family problems” where ranked 10th out of
11 factors affecting labor productivity in UAE. While in
Enshassi et al. (2011), it ranked 8th out of 45.
factors in its group and overall ranking 4th among 40
factors. Furthermore, Gundecha (2012) explains that
equipment/tool shortage is a key factor for laborers to be
able to complete their work, without them the project will
be delayed which results in cost and time overrun. In Iran
Ghoddousi and Hosseini (2012) indicate that shortage
of tools and equipment is one of the top three factors
that affect labor productivity in the Iranian construction
projects. The factor was ranked 1st in the material group
and overall ranking of 3rd among 31 factors.
Table 4. Factors related to the material group
Factors
Tools and equipment shortages
Delay in material delivery on site
Material shortage
Low quality of raw material
Waiting for equipment to arrive
Damaged material on site
Inefficient use of material on site
Increase of material price
Relative importance index (%)
85.79%
83.42%
82.37%
81.84%
81.78%
72.93%
70.36%
62.47%
Rank
1
2
3
4
5
6
7
8
In Egypt, after the 2011 and 2013 revolutions,
contractors have been unsure of the fate of construction
Ranked 4th in the group and 15th overall with a hence they are careful in spending heavily on construction
relative importance index of 79.47% is “working 7 equipment. They resort to hiring most equipment and
days a week without rest”. The outcome supports rely on manual labor for most tasks.
the findings reported by Jarkas and Bitar (2011),
Durdyev and Mbachu (2011) and Enshassi et al. (2011) 3.2.2 Delay in Material Delivery on Site
that working 7 days a week without rest creates an Delay to material delivery on site is ranked 2nd in this
adverse effect on the motivation and physical strength of group and overall ranking 2nd among 41 factors with
labor. Furthermore due to the schedule pressure by the a relative importance index of 83.42%. This result
government and private sector after the 2013 revolution substantiates the results obtained by Zakeri et al. (1996)
to get construction projects completed on time, both where it was ranked 3rd amongst 31 factors. Zakeri
laborers and construction professionals have been working et al. (1996) stated that “irregular payments lead to
more than 5 months without any time off, which may lead poor procurement and remain a serious obstacle in the
to a decrease in motivation and morale.
path of purchasing material on time” in other words
3.1.4 Working 7 Days a Week without Rest
poor procurement planning is the main cause for delay
in materials delivery on site. Waiting for material is a
As shown in Table 3 Absenteeism is ranked 5th in the major factor affecting labor productivity negatively in
human/labor group and 16th overall. The findings agree Egypt, since materials are very important to complete
with Gundecha (2012), where labor productivity was construction tasks, without them the construction process
measured in USA, and this factor ranked 18th out of 40. can be on hold. Most of the suppliers have kept their
The results also agree with Lim and Alum (1995), where prices the same especially after the revolution to make
it was ranked 4th out of 17 human factors affecting labor sure the profit margin is still the same. This then
productivity in Singapore. Lim and Alum (1995) further results in long-term discussions between contractors and
explains that the most absenteeism is caused by laborers suppliers to agree the price.
who do not turn up, where they are either reported on
3.2.3 Material Shortage
medical leave or just taking a day off.
3.1.5 Absenteeism
3.2 Materials Factors
3.2.1 Tools and Equipment Shortages
From Table 4, “tools and equipment shortages” is ranked
1st but also among the 41 factors. The factor was
ranked very highly by respondents, who from discussions
have stated that tools and equipment shortages are a
major factor that affects labor productivity negatively.
In USA, Gundecha (2012) indicated that tools and
equipment shortage was also a very important factor to
labor productivity and it was ranked 2nd between 12
With a relative importance index of 82.37% Material
shortage was ranked 3rd in this group and 5th among 41
factors. An example of material shortage can be shortage
of cement, bricks, and steel reinforcement which can be a
concern as they cause work disruption on site.
In Gaza, Enshassi et al. (2011) material shortage
was ranked 1st in the group and 1st overall between all
45 factors with a relative importance index of 89.47%.
Enshassi et al. (2011) justifies the results by stating
that in most construction projects that take place in
Gaza, the materials have to be imported from Israel,
therefore any closure of crossing points between the
47
two countries causes a delay in material delivered which need it, this process then delays work by making laborers
results in shortage of material. The results were further wait for equipment to be arrive on site.
supported by Kaming et al. (1997) in Indonesia were
it was ranked 1st among all factors that affect labor 3.3 Management Factors
productivity negatively, since materials could cost 50-65%
of the construction cost in high buildings in Indonesia.
Table 5. Factors related to the management group
Kaming et al. (1997) calculated that the average time
Relative imporFactors
Rank
tance index (%)
wasted for unavailable materials is as follow; steel 2.25
Payment
delay
82.76%
1
hours, carpenter 3.51 hours and bricklayer 1.69 hours.
Rework
82.11%
2
In Egypt this factor was ranked high due to the financial
Lack of supervision leadership
80.53%
3
problems the contractors are facing or a shortage in
Incapability of contractor’s site
management to organize site
79.94%
4
credit facilities which is an issue for material procurement.
activities
Another important reason why this factor was ranked
Pick and drop facility
78.68%
5
high is due to delay of payment from client to contractor
Late payment from client to
75.79%
6
contractor
which results in contractor delayed in ordering materials.
Design changes
72.63%
7
Design and schedule changes were another reason why
Unrealistic scheduling
70.46%
8
the factor ranked high by respondents since design and
Offered services for labor (life
69.84%
9
schedule changes cause different material ordering to
insurance, medical care)
Perks (Eid Bonuses, Free Lunch,
complete modified designs. Therefore based on the
69.81%
10
School books for children)
schedule the contractor orders the materials while the
Incentive scheme
69.47%
11
recent government policy after the revolution and the
Inspection delay
67.89%
12
paper work on material procurement has also been
Lack of periodic meeting with
65.79%
13
labor
causing material shortage, since the procedure takes time
Lack
of
training
sessions
for
to be approved by the government.
65.53%
14
laborers
3.2.4 Low Quality of Raw Material
Low quality of raw material is ranked 4th in this group,
with a relative importance index of 81.84% and 8th
among the 41 factors. The results agree with the findings
found in Afghanistan by Kalsum et al. (2010), it was
ranked 1st in the material group and 5th among 68 factors
with a relative importance index of 83.75%. Kalsum
et al. (2010) identified that materials delivered are not
to the standard specified which delays the construction
process since they have to wait for the required and
specific materials to arrive on site. Similarly, in Egypt
Low quality material is an issue that has been around
for a while in the construction industry. Suppliers either
send not specified material or the quality of the material
itself is very poor. Some of the suppliers do this to save
money, and assume that the required material can be
substituted by other standard materials that are cheaper
without noticing. Suppliers also change the cement bags
with imported cement bags to show a high quality cement
is been delivered.
3.3.1 Payment Delay
Payment delay had a relative importance index of 82.76%
and was ranked 1st in this group it was further ranked 3rd
among all factors explored. The result is in agreement
with the finding of Kalsum et al. (2010) in Afghanistan,
where it was ranked 2nd in the group and 6th out of
overall 68 factors. Payment delays are as a result of
unqualified contractors awarded contracts but do not have
the financial capacity to carry out those jobs. An example
is the case of Afghanistan. Similarly, in India and Gaza,
this factor was ranked 2nd in the management group
(Soham and Rajiv 2013; Enshassi et al. 2011). The factor
was further ranked 6th among 45 factors with a relative
importance index of 78.68%. Enshassi et al. (2011)
justifies the result that payment delay affects laborers
mood and “consequently decreases”. As discussion with
respondent showed that the problem is not any different
in Egypt. Some of the construction projects took up to
8-10 months for payments to go through. The laborers
cannot wait more than a week to get paid since they
3.2.5 Waiting for Equipment to Arrive
also had family needs. Most of the contractors pay from
With a relative importance index “waiting for equipment their own pocket to the laborers until they get payed by
to arrive” was ranked 5th in the group and 9th overall the client. When laborers mood decreases, motivation
with a relative important index of 81.78%. Examples decreases and that results in either decrease in laborer
of equipment can include vibrators, bulldozers, backhoe performance or leaving to find another job where they
loaders, cranes, and concrete mixers. Equipment are can get paid on a daily basis.
very important for completely any construction tasks,
as laborers cannot work without them. Waiting for 3.3.2 Rework
equipment can be a serious issue since it can cause delay in Rework had a relative importance index of 82.11% and
daily work and extra cost. With the current situation of was ranked 2nd in the group and 6th among all factors.
the construction industry in Egypt contractors don’t book This effect substantiates the results obtained by Kaming
equipment in advance since they are not sure the project et al. (1997) in Indonesia were the factor was ranked
will keep going on it, they rent the equipment when they 2nd out of 9 factors. Kaming et al. (1997) states that
48
brick-layers and carpenters spending almost double the
time reworking than steel fixers, this is either caused by
design changes, poor instructions, complexity of design
specification, and poor workmanship. In Egypt design
changes and unclear instruction lead to rework. Beside
laborers are working six or seven days a week without rest
which causes physical fatigue, and rework. Respondents
stated that rework is caused by unclear drawings,
supervisor is unaware of job, design complexity, design
changes by client and working overtime. Example of
common problem would be revised construction drawings
send to subcontractor cause rework due to construction
errors. Supervisors and craftsmen have a big role in
this factor, since lack of leadership, skills, and knowledge
results in incorrect information send to the laborers,
where it leads to rework. The time it takes for rework
by the skilled laborer and the time it took by the
inexperienced, unskilled laborer have caused the project
to be delayed.
and Luxor. They are unfamiliar with Cairo city which
therefore not sure how to travel to the construction site.
Pick and drop facility is an issue since it causes high
percentage of laborers absenteeism. Some contractors
send cars to bring laborers to construction site, and
class contractors such “Arab Contractors”, “Orascom
Construction Industry”, and “Hassan Allam” have their
own buses to pick and drop laborers from their homes
to the construction site and back. These pick and drop
facility will save time since all laborers will start and be
at the construction site at right scheduled time.
3.4 External Factors
Table 6. Factors related to the external group
Factors
On site accident
Access to site
Poor site condition
Shortage of power supply/water
Weather (high wind, hot temperature,
rain and sandstorms)
Security (crime and theft)
Insufficient lighting
Regulations change by government
Natural disaster (flood and hurricane)
3.3.3 Lack of Supervision Leadership
The third ranked factor is “lack of supervision leadership”
with a relative importance index of 80.53% and overall
ranking 12th among the 41 factors.
The outcome
supports the findings of Jarkas and Bitar (2011) who
identified that lack of supervision encourages operatives
(especially those who are under the direct employment
method) to engage in unproductive activities, especially
when supervisors leave the site for personal matters.
Similarly, when supervisors are unaware of how to
complete tasks or give instruction this causes lack of
leadership and weak control of laborers. Most of the
supervisors in Egypt’s construction projects are not
thorough and the do not have the proper training in
terms of project control. They arrive late on site, and, or
leave the site early. They also lack leadership skills and
generally poor supervision causes all sorts of problems
on site. This problems includes rework, laborer attitude
problems, problems between workers, and delayed tasks.
Relative importance
index (%)
81.58%
80.79%
78.38%
76.42%
75.00%
71.32%
68.91%
65.26%
60.31%
3.4.1 On Site Accident
Ranked 1st in the group was “on site accidents” with a
relative importance index of 81.58% and overall ranking
10th among 41 factors. It is obvious that on site
accidents causes delay in the construction project. The
results obtained from this research agree with the results
obtained in Gaza by Enshassi et al. (2011). This factor
was ranked 2nd out of 7 in the group and overall ranking
13th out of 45 factors. Enshassi et al. (2011) explained
that there are three types of accidents 1- Accidents
that results in death, 2- Accidents that causes injured
laborers to be hospitalized for more than 24 hours 3small accidents that result from nails and steel, wires
and affect productivity in few cases. Laborer’s careless,
3.3.4 Incapability of Contractor’s Site Management
ignorance, negligence, and lack of attention by contractor
to Organize Site Activities
causes unsafe working environment, which therefore leads
Ranked 4th in the group and 14th out of 41 factors overall, to site accidents. When laborers are injured they delay
with a relative importance index of 79.94% is “incapability the work of the rest of the gang. In Egypt it was stated
of contractor site management to organize site activities”. by respondents that nearly every week there is an injured
A Site manager is responsible to ensure that site has been laborer, either a small accidents or big accidents. Most of
prepared for laborers to be able to accomplish their tasks. them cause the work to stop since all laborers gather to
They should also check the work sequence according see what happened and start chatting. Over confidence
to work programme. Inexperienced site managers in in laborer’s skills has also led to site accidents, where
procurement, leadership, scheduling and planning slows laborers have thought they are aware of all healthy and
down work progress. When site managers cannot organize safety policies on site. They tend to get injured by
site activities it causes delay in construction process.
equipment and tools or falling from height.
3.3.5 Pick and Drop Facility
3.4.2 Access to Site
“Pick and drop facility” had a relative importance index
of 78.68% ranked 5th in the group and 17th overall. As
stated previously that nearly all the construction laborers
come from rural cities that are hundreds of KM away
from Cairo such as Asyut, Aswan, Qena, Sohag, Minya
Ranked 2nd in the group is “Access to site” and overall
ranking 11th out of 41 factors, with a relative importance
index of 80.79%. This effect substantiates the results
obtained by Gundecha (2012) in the USA whose research
placed “Access to site” in the 11th rank among 40 factors.
49
Gundecha (2012) justifies the findings that poor access
reduces the free movement of labor and consequently,
reduced labor productivity.
The majority of the laborers live far away from the
construction site. Getting access to site either because of
transportation or security reasons can be a key element
that affects labor productivity negatively.
Another
important reason why the factor was ranked high was
due to holes and barricades and time spending finding
alternative routes. For security reason the majority of
the laborers are to provide their ID before entering the
site.
3.4.3 Poor Site Condition
Poor site condition is ranked 3rd with a relative
importance index of 78.38% and ranked overall 18th
among 41 factors. Poor side condition can be land height,
shape and ground conditions. Some examples of different
site conditions occur when a contractor performs earth
excavation and different soil types that weren’t previously
seen. Each site is different than another and poor
site condition can cause difficulties and unsafe working
environment, which can result in accidents and delay.
Most of the site conditions are outside the hands of the
project managers control but contractors should take care
of it before the start of the project, which can cost the
contractor extra money.
3.4.4 Shortage of Power Supply/Water
With a relative importance index of 76.42% “shortage
of power supply/water” ranks 4th in the group and
20th overall, one of the main contributes to large
productivity gap between developed and developing
countries is low quality infrastructure. Power supplies
in many African countries have the reputation for high
distribution costs, and unreliability that affects efficiency
and competitiveness (Abdul Kadir et al. 2005). After
the revolution shortage of electricity and water supply
has been an issue; this is as a result of the bombings
of major power station during the revolution. Most of
these problems are out of the contractors hands. If
power and water were available there might be also
other problems such as underground power cables being
stuck by excavators, and water pipes are burst during
excavation work. Gundecha (2012) states that proper
lighting is one of the basic requirements for obtaining
fair labor productivity with any construction work,
failure to have adequate lighting may lead to different
consequences, such as misplacing a particular job, or even
a serious accidents and deaths.
climate was looked at. Most of the African countries are
hot and dry. In Egypt the temperature averages between
26.7o C and 32.2o C in the summer and up to 43o C on
the red coast. In winter the temperature varies on an
average between 13o C and 21o C. In general the weather
in Egypt is hot, dry and humid in the delta coast along the
Mediterranean. Humidity increases in July and August
and spreads through all Cairo.
The majority of North Africa and the Middle East
are hot and dry with an average temperature between
29o C-35o C where laborers are used to working in such
conditions but get tired quickly and need breaks for water
and food. In UAE, Ailabouni (2007) found that weather
condition affects labor productivity negatively. The
temperature in UAE goes up to 42-45o C and a relative
humidity varying from 40-90 and some cases 95%. The
government makes a mandatory break for all construction
workers between 12:30-3:30pm from the period of June
to September to assure the safety of the workers. The
same case was found in UAE’s neighbour Kuwait. Where
findings in Kuwait done by Jarkas and Bitar (2011) has
ranked the factor 11th overall out of 45 factors. The
temperature in Kuwait can reach up to 50o C between
the periods of June-August where the government then
bans work in open environment between 12:00 -16:00.
The rest of the months are normally pleasant with mild
temperatures ranging from low 20o C to low 30o C.
100% productivity can be reached when the
temperature is between 5o C and 25o C and a relative
humidity is below 80% (Zakeri et al. 1996). Since
weather cannot be controlled by contractors, contractors
can overcome the problems by pre fabricating some of
the work.
3.5 Overall ranking
All the factors identified from the survey are ranked based
on their percentage of relative importance. This is shown
in Table 7.
4 CONCLUSION AND
RECOMMENDATIONS
This study described an investigation into different
factors affecting the productivity of labor in Egyptian
construction industry so as to improve the performance
of the industry. 41 factors were identified based on
extensive literature reviews and these factors were further
grouped in 4 major categories: (1) Human/Labor Factors;
(2) Material Factors; (3) Management Factors; and (4)
External Factors.
Survey questionnaires were handed out based on the
3.4.5 Weather
factors identified a total of 258 hard copy questionnaires
The fifth ranked factor was “weather” (high wind, were handed out and 227 were collected back. The data
hot temperature, rain and sandstorms) with a relative from the survey was analysed and the factors were ranked
importance index of 75.00% and ranked 23rd overall. based on their relative importance index. The results
The majority of the construction work is done in open revealed that these 10 factors ranked between RII =
atmosphere and can be seriously affected by unexpected 85.79% and RII = 81.58% are the major factors affecting
weather conditions. To understand why the factor was labor productivity in Egypt. These factors include: (1)
ranked highly by respondents a closer look to Egypt’s Tools and equipment shortages; (2) Delay in material
50
Table 7. Overall ranking of all the factors
Factors
Tools and equipment shortages
Delay in material delivery on site
Payment delay
Indiscipline labor
Material shortage
Rework
Labor experience and skill
Low quality of raw material
Waiting for equipment to arrive
On site accident
Access to site
Lack of supervision leadership
Personal/family problems
Incapability of contractorąŕs site management to organize site
activities
Working 7 days a week without rest
Absenteeism
Pick and drop facility
Poor site condition
Labor motivation
Shortage of power supply/water
Late payment from client to contractor
Arguments between workers
Weather (high wind, hot temperature, rain and sandstorms)
Physical fatigue
Labor age (old/young)
Damaged material on site
Design changes
Communication problems between labor and supervisor
Security (crime and theft)
Unrealistic scheduling
Inefficient use of material on site
Offered services for labor (life insurance, medical care„ „)
Perks (Eid Bonuses, Free Lunch, School books for children)
Incentive scheme
Insufficient lighting
Inspection delay
Lack of periodic meeting with labor
Lack of training sessions for laborers
Regulations change by government
Increase of material price
Natural disaster (flood and hurricane)
delivery on site; (3) Payment delay; (4) Undisciplined
labor; (5) Material shortage; (6) Rework; (7) Labor
expensive and skills; (8) Low quality of raw material;
(9) Waiting for equipment to arrive; and (10) On-site
accident.
From these findings the following recommendations are
made as ways of improving and reducing the factor that
affect labor productivity, they are:
1. Investment in people is very valuable especially in a
country like Egypt with a relatively high population
and an abundance of manpower. Government
policy should pay attention to secondary technical
education and apprentice programs.
2. Government need to provide rules and regulation
which will help create a safe working environment
for laborers such as obliging companies to provide
minimum wages and insurance coverage against
accident during work. This can be agreed with
the “Egyptian Trade Union Federation” to make
sure the laborers are under the umbrella of working
in safe environment.
Government could also
51
Relative importance index (%)
85.79%
83.42%
82.76%
82.63%
82.37%
82.11%
81.96%
81.84%
81.78%
81.58%
80.79%
80.53%
80.37%
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
Factor group
Material
Material
Management
Human/Labor
Material
Management
Human/Labor
Material
Material
External
External
Management
Human/Labor
79.94%
14
Management
79.47%
79.21%
78.68%
78.38%
77.62%
76.42%
75.79%
75.67%
75.00%
74.47%
74.27%
72.93%
72.63%
71.58%
71.32%
70.46%
70.36%
69.84%
69.81%
68.95%
68.91%
67.89%
65.79%
65.53%
65.26%
62.47%
60.31%
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Human/Labor
Human/Labor
Management
External
Human/Labor
External
Management
Human/Labor
External
Human/Labor
Human/Labor
Material
Management
Human/Labor
External
Management
Material
Management
Management
Management
External
Management
Management
Management
External
Material
External
provide industry wide seminars and workshops that
promote Health and Safety issues. In this way
accidents on site will reduce due to the laborers
are more familiar with the Health and Safety
regulations.
3. Contractors should support laborers for regular
training and for the craftsmen to keep them up to
date and aware of skills which have to be improved.
4. Improve labor motivation by paying them a fair
wage that they and their families can live from
with the cost is increasing. That could be done
by developing an Incentive scheme programs were
workers will know that tasks completed on-time
with the standard required will result in bonuses
and will also increase laborer’s loyalty and moral of
laborers. This can also be done by developing good
work schedules that respect workers home needs
both local to area and external to area. This means
provide balance between safe site and happy life.
5. Stakeholder
should
adopt
collaborative
construction procurement approaches such as
Design and Build Alliances. This would enhance Jarkas, A. M. and Bitar, C. G. (2011). “Factors affecting
the constructability of the design thus facilitate the
construction labor productivity in kuwait.” Journal
production process; enhance communication and
of Construction Engineering and Management, 138(7),
coordination between project parties in which turn
811–820.
enhances the flow of activities.
Kalsum, U., Hanid, M., Zakaria, N., Yahya, Z.,
and Lia, P. C. (2010). “Assessing the performance
of construction workers in Peninsula Malaysia.”
International Journal of Engineering and Technology,
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52
Vol 5, No 1, March 2016, 53-60
Compensation Mechanism for Early Termination of
Highway BOT Projects Based on ARIMA Model
Jingbo Song ∗ , Yanan Fu and Ousseni Bagaya
Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, Liaoning, China
Abstract: Due to the incomplete characteristic of concession agreements and unpredicted events, such as
decision-making mistakes and force majeure, many Build-Operate-Transfer (BOT) projects were terminated
before the expiry of the concession period. In general, the government and the private sector will have a quite
laborious negotiation on the compensation clauses. In a bid to avoid the endless disputes and huge losses,
this paper proposes a quantitative compensation model for early termination of highway BOT projects by the
analysis of cash flow. An ARIMA model has been constructed to forecast the future traffic volume, operation
and maintenance cost, and future cash flow. The compensation amount is determined based on the realized
revenue and estimated future revenue. By adopting a numerical example, this paper illustrates the application
of the proposed model and provides a reference on compensation for early termination of highway BOT projects
for both the government and the private sector.
Keywords: Build-Operate-Transfer, early termination, ARIMA model, compensation
DOI: 10.7492/IJAEC.2016.006
1 INTRODUCTION
There has been a move toward increased reliance
on Build-Operate-Transfer (BOT) for infrastructure
development, which aims at enhancing profitability, reducing deficit/debt and overcoming broad public sector
constraints in relation to a lack of capital or capacity
to develop, manage, and operate infrastructure assets
(Akintoye et al. 2003; Zhang and Kumaraswamy
2001; Zhang 2005). However, many BOT projects
are characterized by large scale investments, various
stakeholders and long concession periods, which results
in high and multi-dimensional risks and uncertainties
(Liu et al. 2014; Cruz and Marques 2012). In
practice, hasty and unreasonable decision and lack of
well-designed risk allocation arrangements make many
projects difficult in recovering investment and face with
renegotiations, even fail to operate (Guasch 2000).
Early termination means stopping the original contract
and quitting the concession. Results from recent
research for risk prioritization have shown that the
termination of concession is one of the most important
risks for infrastructure construction (Valipour et al.
2015; Zhang and Xiong 2015).
Nowadays, early termination of BOT projects are
very common. The World Bank (2011) has found
that 334 of the 4,874 PPP projects undertaken in
developing countries from 1984 to 2010 were early
terminated. Even some famous projects, such as
National Physical Laboratory (Leahy 2005; House
of Commons 2006), Skye Bridge project (Moles and
Williams 1995; Whitfield 2011), Channel Tunnel
(Ho 2006; Ho and Tsui 2010) in the U.K. and
Camino Colombia Toll Road (Samuel 2003), South
Bay Expressway (Samuel 2011) in the USA, were
terminated before the expiry of the concession period
due to a variety of risks and unreasonable decisions.
In general, the liability on early termination can be
mainly attributed to the private sector, the government
or force majeure. Standardization of PF2 contract
in the U.K. indicates that the concession contract
should deal comprehensively with the consequences of
compensation and transfer of the remaining project
assets (HM Treasury 2012).
With the frequent occurrence of early termination
for BOT projects, decision-making on compensation,
as the most concerned issue for the government
and private sector, has attracted many researchers’
attention (Chen and Doloi 2008; Chen 2009; Demirag
et al. 2011; Xiong et al. 2015). The compensation
amount for early termination depends on the cause
of early termination: If the early termination is
53
Song et.al./International Journal of Architecture, Engineering and Construction 5 (2016) 53-60
caused by the private sector’s default, the payment is
normally the market value of the project, which can be
determined by the rebidding price of the contract. If
it is caused by force majeure (e.g., a natural disaster),
the government normally pays an amount linked to the
private sector’s debt and, in some cases, to the book
value of its equity. If it is caused by the government’s
default, the government should fully compensate
lenders and shareholders for their losses (Irwin and
Mokdad 2009). Kolleeny (2007) indicated that the
concession agreement must provide clear rights for the
concessionaire in the event of early termination on any
grounds (including the private sector’s own default)
to receive immediate payment from the concession
authority of the full, fair market value of all assets.
Alonso-Conde et al. (2007) presented a valuation
model of the Melbourne City Link Project to calculate
the compensation of early termination by using real
options theory. Aiming at the buyback issue of toll
road, Ren and Li (2011) constructed a bargaining game
model between the government and the private sector.
Xiong and Zhang (2014a) introduced two popular
compensation approaches, which are compensation
based on the financial statement and discounted value
of future cash flow. By studying the Melbourne
City Link Project, Arndt (1998), Lay and Daley
(2002) pointed out that the compensation amount
should include the construction cost of the project,
operating expenses and the cost of capital of the project
sponsor. Besides, if an agreement is terminated, the
compensation amount must be proportional, a matter
to be assessed separately in every case. For the
compensation, issues to be considered also include
duration of the agreement, past and expected future
value of the agreement for both parties, reason for
termination, gravity of the breach of agreement (Iossa
et al. 2007; Talus 2009).
company and force majeure event) are summarized
in Figure 1. An early termination scenario means
an event that has occurred and forced the project
to terminate before the expiry of concession period.
Among them, the expropriation and nationalization
are regarded as the main termination causes, under
which private sectors can do nothing but ask for
warranties and compensation in a long-term contract
(Efficiency Unit 2003). In this case, it’s reasonable for
the government to judge whether the actual revenue
of the project reaches the break-even point before
paying the compensation. If the actual revenue does
not reach the break-even point, the private sector
could not recover the initial investment, and therefore,
compensation amount should include unrecovered
investment and, in some cases, a part of future revenue.
If the actual revenue reaches the break-even point,
the private sector has recovered the initial investment,
and the future revenue should be shared between the
government and private sector. Such compensation
measures are fair and beneficial for both of them.
Figure 1. The compensation mechanism for early
termination of BOT projects
Assuming that a highway BOT project has a
concession
period of T years, including a construction
As mentioned above, no matter what methods have
period
of
m
years, the project has been terminated
been proposed, the termination causes and revenue
at
the
T
year.
The key issue in developing
f
forecasting are always the significant factors for
the
quantitative
compensation
model is to estimate
determining the compensation. To accurately calculate
the
future
cash
flows.
The
research
hypotheses in
the reasonable compensation amount, this paper
developing
the
model
are
discussed
in
the following
applies ARIMA model to forecast the traffic volume
sections.
and O&M cost, then, proposes the compensation
Hypothesis 1: There are many stochastic variables
solution through calculating the discounted value of
affecting
the cash flows of the project, of which the
estimated future cash flows.
It also provides a
traffic
volume
and O&M cost are the most important,
reliable quantitative method on compensation of early
and
other
stochastic
variables are not taken into
termination in order to smooth the negotiation process
consideration
in
this
study.
of highway BOT projects for the government and the
Hypothesis 2: All of the vehicles belong to the
private sector.
same type and there is no congestion during the whole
operation period. Let N P V1 denote the accumulated
net present value (NPV) over the last Tf years, thus,
2 COMPENSATION MECHANISM
N P V1 can be determined as follows:
AND RESEARCH HYPOTHESES
Through the classification of liability of early termination, several early termination scenarios (such as
government’s expropriation, bankruptcy of the project
54
N P V1 = −
m
X
t=1
Tf
X
It
P Qt − Ct
+
(1 + r)t t=m+1 (1 + r)t
(1)
where It is the construction cost, Qt is the actual
traffic volume, P is the actual toll rate, Ct is the actual
O&M cost and r is the discount rate of the project.
If the highway BOT project hasn’t been terminated
earlier but is still operated by the private sector till the
expiry of the concession agreement, the accumulated
NPV of the project in the whole concession period can
be calculated as follows:
0
0
T
X
P Qt − Ct
(2)
N P V2 = N P V1 +
(1 + r)t
model is used for analyzing non-stationary time series.
ARIMA model was firstly proposed by Box and
Jenkins in the early 1970s (Box and Jenkins 1976).
As a famous time series prediction method, ARIMA
model includes autoregressive model AR (p), moving
average model MA (q), autoregressive moving average
model ARMA (p, q) and ARIMA model (Zhang 2003).
AR (p) can be expressed as φ(B)ωt = at , φ(B) =
(1 − φ1 B 1 − φ2 B 2 − · · · − φp B p ), φ(B) is the AR
operator, p is the order of the AR model, at is the white
t=Tf +1
noise
time series sampled from a random variable with
where N P V2 is the accumulated NPV in the whole
0
mean
0 and finite variance σ 2 . MA (q) is expressed as
concession period, Qt is the estimated traffic volume,
1
2
q
0
Ct is the estimated O&M cost. This paper only ωt = θ(B)at , θ(B) = (1 − θ1 B − θ2 B − · · · − θq B ),
θ(B) is the MA operator, q is the order of the MA
considers the situation of N P V2 ≥ 0.
model. ARMA (p, q) is described as φ(B)ωt = θ(B)at
Let GP denote the compensation amount for the
which combines both the AR (p) model and MA (q)
private sector and assuming that GP is a one-time
model. Based on it, ARIMA model can be expressed
payment at the year of Tf + 1. If N P V1 < 0, which
as:
means the actual revenue does not reach the breakθ(B)
at
(5)
(1 − B)d X(t) = µ +
even point and the private sector could not recover
φ(B)
the initial investment, thus the compensation amount
where d = the order of differencing. µ = mean value
should include two parts, which are the unrecovered
investment and a part of future revenue. The first of transformed stationary time series (1 − B)d X(t).
part GP1 should compensate for the unrecovered The procedures for applying ARIMA model to forecast
investment, which means GP1 should satisfy N P V1 + traffic volume and O&M cost are discussed in the
GP1
= 0 , while the second part GP2 is a part of following:
(1+r)Tf +1
(1) Series test and transformation. Augment Dickeyfuture revenue.
Fuller
(ADF) unit root test is usually adopted to
Let α be the private sector’s sharing ratio of the
examine
whether the time series is stationary. If the
future revenue, and it can be determined by negotiating
ADF
test
value of series is greater than the critical
with the government. Thus, compensation amount GP
value
of
unit
root test, time series is non-stationary.
can be expressed as:
Any non-stationary time series data must be transGP = GP1 + GP2
formed into stationary data. Several methods such as
= −(1 + r)Tf +1 N P V1 + αN P V2
(3) Box-Cox transformations, classical decomposition and
If N P V1 ≥ 0, which means the actual revenue has differencing can be used to transform the data series.
reached the break-even point and the private sector In this study, differencing method is used to do the
has recovered the initial investment, the compensation transformation, and normally the order of differencing
GP should be only a part of the future revenue. Thus, is no more than 2.
(2) Model selection. After transforming the original
GP can be determined as follows:
data series into stationary data series, an ARIMA (p,
GP = α(N P V2 − N P V1 )
(4) d, q) model must be selected to fit the data series. The
0
0
If the future traffic volume Qt and O&M cost Ct initial values of parameters p and q can be determined
were predicted, N P V2 can be calculated smoothly. By from the autocorrelation function (ACF) and partial
considering the Eq. (3) or Eq. (4), the compensation correlation function (PACF) graphs. If the PACF
amount GP can be derived. So the core issue in of the time series cuts off after
√ lag p and the ACF
0
0
reduces
to
the
bounds
±1.96/
n, which are the upper
determining GP is to forecast Qt and Ct .
and lower 95% confidence limits, the time series is
AR (p). If the ACF of the time series cuts off after
3 ARIMA MODEL
lag q and the PACF reduces to the bounds, the time
series is MA (q). If both the ACF and PACF of the
0
0
Obviously, the traffic volume Qt and O&M cost Ct time series reduce to the bounds, the time series is
are dynamic time series data. In order to accurately ARMA. However, the identification of the model type
forecast them, a time series analysis is conducted based on these functions is not always feasible, so it
based on the relationship between the actual value is necessary to chose other criterion to determine the
and future value, which is an effective way for dealing fitting model. The criterion widely used in modelwith dynamic data. However, the traffic volume and selection processes is Bayesian Information Criterion
O&M cost could be non-stationary time series while (BIC) or Akaike Information Criterion (AIC). The BIC
the traditional time series model can only describe the or AIC is an estimation of the information lost when
changing trends of stationary time series, thus ARIMA a given model is used to represent the process that
55
Table 1. Operation data of project S
Year
0
1
2
3
4
5
6
7
8
9
10
11
Traffic volume
(vehicles per day)
Revenue
(million RMB)
12,762
14,741
16,127
17,845
20,084
22,027
24,276
25,606
27,994
31,145
91.88
107.61
117.72
130.26
146.61
160.79
177.21
186.92
204.35
227.35
O&M cost
(million RMB)
1,000.00
1,000.00
20.30
22.99
24.92
27.75
30.83
33.53
36.62
38.51
41.79
46.07
NPV
(million RMB)
-1,000.00
-925.92
61.37
67.17
73.67
75.35
78.80
86.61
88.59
86.59
87.83
90.69
Accumulated NPV
(million RMB)
-1,000.00
-1,925.92
-1,864.55
-1,797.38
-1,723.71
-1,648.36
-1,569.56
-1,482.95
-1,394.36
-1,307.77
-1,219.94
-1,129.25
actually generates the data (Xiong and Zhang 2014b). 4.1 Series Test and Transformation
So the model with minimized BIC or AIC is the best
The Eviews 6.0 is used to conduct the time series
fitting model.
analysis. The original time series graphs of traffic
(3) Model testing. Autocorrelation (AC) test and volume and O&M cost from 2003 to 2012 are showed in
partial correlation (PAC) test need to be done in this Figure 2, in which both two data series have the longsection. If all P values of the Q statistics of the term increasing trends, thus they are non-stationary
residual series test are greater than 0.05, the residual and have to be transformed into stationary series.
series of the original data set are white noise, and the Differencing method is applied for both the two data
chosen model is fitted. Otherwise, the model should be sets. Results show that the time series after the
first differencing are still non-stationary, so differencing
modified.
treatment has to conduct again. The test results of the
(4) Prediction. After the best fitting model has quadratic differencing series are shown in Table 2. The
been selected, the final step is to use the determined ADF Test statistics of traffic volume and O&M cost
ARIMA(p, d, q) model to forecast the future traffic are correspondingly -3.03 and -3.15, which are both
volume and O&M cost data.
less than the critical value of the significant level 1%.
The test results indicate that the transformed series
have become the stationary series. So the differencing
order d is set to be 2. The transformed data series of
4 NUMERICAL EXAMPLE
traffic volume and O&M cost are plotted in Figure 3.
S is a highway BOT project which was built in 2001, Table 2. ADF Test of the transformed data series of
traffic volume and O&M cost
with 2 billion RMB investment and a planned 27year concession period including a 2-year construction
Traffic volume O&M cost
Significant level
period. In the operation stage, the actual traffic
-3.03
-3.15
volume was much lower than the estimated traffic
1% level
-2.93
volume. The private sector suffered from great losses
5% level
-2.00
over the last 10 years. Therefore, the government
10% level
-1.59
and private sector agreed to terminate the concession
agreement by consensus. To achieve win-win outcomes
for both the government and the private sector, 4.2 Model Selection
the government decided to terminate the concession
After transforming the original data series into
agreement and compensate the private sector.
stationary data series, the next step is determining the
The operation data of Project S from 2001 to 2012 optimal parameters p and q. The ACF and PACF
are shown in Table 1. The toll rate P is 20 RMB per graphs are computed based on the transformed data
vehicle, which is the average toll rate of all vehicle types sets. Both the traffic volume model and O&M cost
in proportion to their traffic volume. The discount model are initially defined as ARIMA (0, 2, 0) as
rate is assumed to be 8% for highway projects (Hu and both the ACF and PACF reduce to the bounds after
Cao 2010). The accumulated NPV in 11 years is -1.12 lag 0. Furthermore, BIC is used to selected the best
billion RMB, so there is still a long term to recover the fitting model. Table 3 presented the various models
initial investment.
with different orders and corresponding BIC values.
56
Figure 2. Original data series of traffic volume and O&M cost
Figure 3. Transformed traffic volume and O&M cost data series
Figure 4. ACF and PACF graphs of the transformed traffic volume and O&M cost
ARIMA (0, 2, 1) has the minimized BIC, so it has
been selected for both the traffic volume model and
the O&M cost model.
4.3 Model Testing
The time series model is appropriate if the residual
forms a white noise time series data set. Autocorre-
lation test and partial correlation test results of the
residual series of the traffic volume and O&M cost are
presented in Table 4. All P values of the Q statistics of
the residual series test of traffic volume and O&M cost
are greater than 0.05, therefore, the residual series of
both the traffic volume and O&M cost are white noise
time series data set. ARIMA (0, 2, 1) model are fitted
appropriately.
57
Table 3. Summary of selected time series models
Traffic volume
model
ARIMA (0, 2, 0)
ARIMA (1, 2, 0)
ARIMA (0, 2, 1)
ARIMA (1, 2, 1)
BIC
O&M cost
model
ARIMA (0, 2, 0)
ARIMA (1, 2, 0)
ARIMA (0, 2, 1)
ARIMA (1, 2, 1)
13.559
13.875
13.539
14.098
BIC
9.564
9.869
9.563
10.114
Table 4. Autocorrelation and partial autocorrelation test of the residual series
Lag
1
2
3
4
5
6
7
Test of the traffic volume residual
AC
PAC
Q statistics
0.005
0.005
0.0002
-0.188
-0.188
0.4737
-0.516
-0.533
4.7370
0.052
-0.043
4.7907
0.116
-0.114
5.1511
0.028
-0.358
5.1825
0.004
-0.042
5.1838
series
P
0.988
0.789
0.192
0.309
0.398
0.521
0.638
Test of the O&M cost residual series
AC
PAC
Q statistics
P
-0.114
-0.114
0.1476
0.701
-0.344
-0.362
1.7277
0.422
-0.137
-0.270
2.0275
0.567
-0.268
-0.594
3.4665
0.483
0.397
-0.026
7.6767
0.175
0.114
-0.314
8.2010
0.224
-0.149
-0.256
9.9731
0.190
Table 5. Predicted data of traffic volume and O&M cost and NPV analysis
Year
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Traffic volume
(vehicles per day)
34,260
37,789
41,784
46,301
51,394
57,115
63,521
70,663
78,598
87,379
97,059
107,694
119,337
132,042
145,863
Revenue
(million RMB)
250.09
275.86
305.02
337.99
375.17
416.94
463.70
515.84
573.76
637.86
708.53
786.16
871.16
963.90
1,064.80
O&M cost
(million RMB)
50.12
54.61
59.59
65.11
71.24
78.01
85.49
93.74
102.80
112.73
123.58
135.41
148.27
162.21
177.30
4.4 Prediction
After the models have been developed, the future traffic
volume and O&M cost can be forecasted respectively.
The traffic volume and O&M cost from 2013 to 2027
are predicted with a 95% confidence interval and listed
in Table 5. The toll rate is fixed at 20 RMB per vehicle
in the future. The result shows that the traffic volume
increases and reaches more than 140,000 vehicles per
day, while the O&M cost increases and reaches about
0.17 billion RMB. The traffic volume increases due to
the economic development, population increase, and
so on. As for O&M cost, it increases because of more
repair and rehabilitation activities.
NPV
(million RMB)
92.62
94.89
97.46
100.34
103.47
106.84
110.39
114.08
117.85
121.68
125.50
129.27
132.96
136.54
139.95
Accumulated NPV
(million RMB)
-1,036.63
-941.74
-844.28
-743.94
-640.47
-533.63
-423.24
-309.16
-191.31
-69.63
55.87
185.14
318.10
454.64
594.59
N P V1 and the predicted traffic volume and O&M cost
in the remaining concession period (from 12 to 26
years), is 594.59 million RMB. N P V1 < 0 means the
private sector did not recovered the initial investment,
thus compensation should include the unrecovered
investment and a part of furure revenue. According to
the Eq.3., the first part GP1 to fill up the unrecovered
investment is 2,437.96 million RMB. Assumed that α
is 0.4, so the private sector’s sharing of the part of
future revenue GP2 is 237.87 million RMB. The final
solution of compensation amount GP is 2,675.83 billion
RMB. It means that the government should pay a total
amount of 2,675.83 billion RMB at the year of Tf +1 as
one-time payment to the private sector.
4.5 Compensation solution
5 CONCLUSIONS
From Table 1 and Table 5, it is clear that the N P V1 of
Project S over the last 11 years is -1,129.25 million In this paper, an ARIMA model is proposed to forecast
RMB and the N P V2 , which is calculated based on the key influencing variables of compensation for BOT
58
Highway projects. To make the compensation amount
more feasible, the liabilities of early termination and
realistic situation of actual revenue of project should be
considered carefully. In the case of expropriation and
nationalization, if the actual revenue does not reach
the break-even point, the compensation amount should
include unrecovered investment and a part of future
revenue. If the actual revenue reaches the break-even
point, the government and private sector should fairly
share the future revenue by a proportion. Based on it,
the cash flow analysis is conducted and the dynamic
cash flow for the future is predicted by ARIMA model.
A numerical example is employed to illustrate the
application of the proposed models. The application of
time series analysis shows that the dynamic variables
can be satisfactorily fitted to the selected time series
model. It is also shown that the ARIMA model is
appropriate to deal with the time series data such as
traffic volume and O&M cost, by which the proposed
model provides a reference on compensation for early
termination of highway BOT projects.
For the simplicity, this paper didn’t try to take
the liabilities of early termination into consideration
and just considered the case of expropriation and
nationalization. In the early termination of a default
by private sectors, the compensation mechanism will be
more complex, and it is worth studying in the future.
ACKNOWLEDGMENT
This work is supported by the National Natural
Science Foundation of China (Grant Nos. 71272091
and 71472022) and the Fundamental Research Funds
for the Central Universities (DUT14RW210 and
DUT15RW148).
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