Anna Brook1 and Eyal Ben-Dor2 Remote Sensing and GIS Laboratory

Anna Brook1 and Eyal Ben-Dor2
Remote Sensing and GIS Laboratory
1The Porter
School of Environmental studies
2Department of Geography and Human Environment
Tel-Aviv University
Modern high performance concrete - HPC
The HPC is characterized by superior tensile
properties and enhanced durability against severe
environmental conditions offering a versatile and
economic solution to many construction projects
2
Modern high performance concrete - HPC
• Hydration - Term that describes a range of reactions
between cement and water to produce the required
strength
• Curing - The process of maximizing the hydration
of the cementations binder to achieve the
intended design parameters
• Hardening - Chemical admixtures are the ingredients
in concrete to modify the properties of hardened
concrete, to ensure the quality of concrete during
mixing, transporting, placing, and curing, and to
overcome certain emergencies during concrete
operations
3
Current Concrete Analysis Methods
• HPC are well characterized by their composition, documented
in 'test samples' report from laboratory specimens and trial
castings
4
The Problem
In order to resolve specific problems that concern
basic global urban environmental indicators, a new
approach, including methods for in situ near real
time analysis, is required!
5
Concrete Lab Test
Concrete Cylinders, and Grout Cubes, are tested for strength
according to the requirements of the WSDOT Standard
Specifications. Concrete is also mixed in this lab and tested
for Slump, Yield, Temperature, and Air Content
6
The Solution
1) Diffuse reflectance spectroscopy
2) The hyperspectral remote sensing
Tools to assess the status and the
strength of the concrete in situ
7
8
Spectral Models
Spectral Database
Standardized and
normalized by an
internal standard
3700 spectra
Spectral Preprocessing
Input spectral data
Test Set
Prediction
Calibration set
Logistic Regression
Neural Networks
Nonlinear iterative
partial least squares
Prediction ability evaluation
Spectral
Model
9
Logistic Regression
• Selecting and Preparing the Input Dataset:
0.5
Log(1/R’)
/R’)
– spectral resampling
– features selection algorithms
0.45
– spectral indices that produces
Data for training
subset
• 0.4Statistic tests and models (M_ANOVA)
0.35
According to the spectral data we defined specific
spectral regions where high and low spectral
0.25
variations
occur
using
a1400
PC analysis,
and
spectral
400
600
800
1000
1200
1600
1800
2000
2200
2400
index
in VIS (nm)
region
Wavelength
0.3
10
Logistic Regression
Data Set 1
Convert
reflectance
spectrum
(R’) to
Log(1/R’)
Data Set 2
Data Set 3
Physical/Chemical
Properties
Spectral
Indices
Multiple Analyses of
Variance (M_ANOVA)
PCA
Spectrally
Active Regions
Partial Least Square Regression (PLS)
Multinomial Logistic Regression
Hydration Model
Curing Model
Hardening Model
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Logistic Regression
Hydration Model
Curing Model
Hardening Model
Physical
property
mixture
water-cement
(w/c) and sand-cement (s/c) ratios
Hardening:
PVAA
(Celvol
0-0.8%
Curing period:
0,and
3,
5 and805)
7components:
days
at an early
age
Training
1000
Trainingdataset:
dataset:
350spectra
850spectra
spectra
Testing
800
spectra
Testingdataset:
dataset:
300spectra
350
spectra
R2 = 0.9582
Variable DF DFParameter
Parameter
Standard Wald
Pr >Pr >
Variable
Standard
Wald
Variable
DF
Parameter
Standard
Wald
Estimate
Error
ChiChi-ChiEstimate
Error
ChiEstimate
Error
Square
ChiSquare
Square 0.0001
Square
INTERCPT 1
2.4563
0.3942
12.4245 Square
INTERCPT 1 1 15.9435
0.4563
9.47320 0.0015
0.0115
10.12853
INTERCPT VIS slope
1-0.9452
-4.2195 0.5984 0.2104
10.0321
VIS slope
1
1.68743
0.1298
11.7637
0.0015
Iron oxides 1
-1.0839
0.4738
11.38477
0.0011
Hardener in SWIR
1
11.99087.215690.53828 0.4932
7.40134
4.372719
Liquid Clay
1 1 2.30556
0.4982
13.0466 0.001
0.0001
region
water
Hardener in SWIR 2
1
12.8642
0.1023
8.94315
No Change
region
<0. 5%
Hygroscopic water (class) 1
0.36021
0.0022
13.799
0. 5-1%
1-2%
Standardized
Odds
Standardize
Odds
Pr >
Standardized
Estimate
Ratio
d Estimate
Ratio
Chi-
-Square
0.46397
0.0015
0.3927
0.39483
0.0001
0.47932
0.2053
Estimate
4.957
2.043
5.477
0.1932
3.875
7.309
Odds
Ratio
12.92
0.001
0.0932
13.92
0.0015
0.1127
4.32
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Logistic Regression – in situ
1m
Hardening (%)
0.23
Progressive
0.25
0.25
Middle
0.23
Middle
0.23
0.23
0.5 m
Hydration
stage; w/c
Hydration
stage; w/c
Hardening (%)
Progressive
0.25
0.25
Early
0.21
0.2
Early
0.2
0.21
Aggregates and Sand in kg
Coarse
(1-2 cm)
100
Water/Cement Ratio
Free Air in Balk (%)
Hardener (%)
0.3
2.3
0.23
Fine (100Sand
500 mm) (200 µm)
50
90
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Neural Network
Input nodes
Hidden nodes
Training (correct rate %)
Test (correct rate %)
Output nodes
First 8 PCs
3
100
100
3
First 5 PCs
3
99
95.7
3
First 4 PCs
3
98.6
95.1
3
First 4 PCs
2
82.9
79.5
3
14
Strength - Nonlinear Iterative Partial Least
Square (NIPALS) Model
Compressive strength (in MPa)
N (concrete samples)
60
Mean strength (MPa)
STD
RPD
8
21.4
2.1
4.19
7
36.6
1.8
3.89
8
47.1
2
4.56
9
54.5
1.6
4.77
9
67.3
1.1
4.59
9
71.2
1.3
4.68
The laboratory results
55
50
45
40
35
Casting concrete day 21 - 352 spectra
30
5
10
15
Sample1
20
Sample 2
25
30
Sample 3
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Hyperspectral Imagery
16
Classification Algorithm
Spectral angle mapper (SAM)
Mixture tuned match filtering (MTMF)
Maximum-likelihood (ML)
Minimum distance (MN)
Neural networks (NNs)
SAM
Support vector
machine (SVM)
NNs
WvA
Wavelet analysis
(WvA)
Input HS image
PPI
Class “Concrete”
Linear unmixing
analysis
Pure “Integrated”
PPI
Pure “Hydration”
“Hydration”
Spectrum
SAM (with threshold)
Possible
Unmixing
>0.1
DR
SVM
MTMF
Binary
MN
ML
Mask - 0
Binary Mask - 1
SAM
MTMF
ML
MN
LR model
2
NNs P(Hydration)
SVM
WvA
0.95
0.47
0.27
0.26
0.77
0.62
0.59
Add to Binary
0.63 - 1 0.47
Mask
0.49
0.91
0.86
0.88
Add to Binary
Area - 0 0.97
Mask
<0.1
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Classification Algorithm
Orange – hydrated concrete (prefabricated concrete walls)
Magenta – early stage of hydration (crossbeams and windows frames)
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Quality Assessment
Aggregates & Sand (in kg)
Coarse
(1-2 cm)
90
Fine
(100-500 mm)
40
Sand
(200 µm)
80
Fine/Coarse
Ratio
0.5
Water/Cement
Ratio
Free Air in
Balk (%)
Hardener
(%)
0.3
1.9
0.3
Yellow- 0.3% w/c, Blue - 0.28% w/c, Green - 0.25% w/c
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Case Study – in situ (21 days after casting)
Aggregates & Sand (in kg)
Coarse (1-2
cm)
100
Fine
(100-500 mm)
50
Sand (200 µm)
90
Fine/Coarse
Ratio
0.5
Water/Cement
Ratio
Free Air in
Balk (%)
Hardener
(%)
0.3
2.3
0.23
Red
is a–progressive
stage ofofhydration
- 0.25%
w/c walls
Green
hydrated concrete
prefabricated
concrete
Blue
early
stage
of hydration
- 0.2% w/c and windows frames
Red is– an
early
stage
of hydration
of crossbeams
Green is prefabricated concrete walls
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