Yuxing Chen`s Complete Thesis Booklet

Swarm Intelligence in Architectural Design
Yuxing Chen
Advisor:
Ronald Rael
Raveevarn Choksombatchai
Content
Chapter 1: Introduction of swarm
1.1
Thesis Statement
2
1.2
Swarm Behavior
3
1.3
Mathematical Models
6
1.4
Boids System
7
1.5
Swarm Intelligence
9
Chapter 2: Swarm algorithms and application
2.1
Swarm Intelligence in Stadium design
12
2.2
Swarm used at Design (by Tyler Julian Johnson)
14
2.3
Swarm Intelligence at Visualization (Robert Hodgin)
18
2.4
Complexity of Swarm at Arts
22
2.5
Object to Field
25
2.6
Swarm Tectonics
29
2.6
Swarm Urbanism (Neil Leach)
31
2.7
Swarm Modeling
33
2.8
Motion at Architecture Design
34
2.9
Particles at Architecture Design
35
Chapter 3: Swarm Tectonics
3.1
Swarm Testing
42
3.2
From Simulation to Application
44
3.3
Swarm Structure
51
3.4
Skin Attachment
60
3.5
Swarm Modular
63
3.6
Swarm Joint
68
3.7
Swarm Drawing
74
3.8
Data Swarm
90
3.9
Conclusion
97
Bibliography
98
Chapter1: Introduction
1
1.1 Thesis statement
Swarm behavior is a collective behavior exhibited by animals of similar size
which aggregate together. It can be applied to any other animal that exhibits
swarm behavior.
Swarm intelligence is the collective behavior of decentralized, self-organized
systems, natural or artificial. The basic idea of swarm intelligence is a “population” of local interactions to the environment in a greater amount that creates
a global system.
At 1989, the swarm intelligence expression was first introduced at robotic systems, which describe the emergent collective behavior. Nowadays this new approach of collective behavior has diversely implemented in many perspective
from biology, social structure, engineering, artificial, visualization and architecture. What’s more, a collection of people can also exhibit swarm behavior, such
as pedestrians.
From the book, “Emergence”, by Steven Johnson, he also wrote about creating a
“form” of living on having emergence logic from the smallest scale as ants into
a larger scale as cities. He tries to define how the swarm logic of ants’ behavior
could give example of the way of living for human level.
For architects, swarm intelligence examines the role of agency within generative design processes. Digital parametric programs like processing are used
to make high populations of self-organized elements into an emergent intelligence. The gold is to explore the dissolution of modernist tectonic hierarchies.
The research about application of swarm principles at architecture ranges from
visualization, self-organization of multi-agent system, architecture form design, urbanism, etc.
Based on the boids system at the digital animation tool, we can simulate the
swarm activity, translate the animal particle system with architecture tectonic system and research about architecture form, structure and space. Swarm
could be generated into an architecture design method.
2
1.2 Swarm Behavior
(http://en.wikipedia.org/wiki/Swarm_behaviour)
As a term, swarming is applied particularly to insects, but can also be applied
to any other animal that exhibits swarm behaviour. The term flocking is usually
used to refer specifically to swarm behaviour in birds, herding to refer to swarm
behaviour in quadrupeds, shoaling or schooling to refer to swarm behaviour in fish.
BY BRENDAN SEIBEL
BIRD FLOCKING
3
BY OCTAVIO ABURTO
FISH SCHOOLING
4
FROM MAASAI MARA KENYA NATIONAL RESERVE
BUFFALO HERDING
5
SWARM INTELLIGENCE IN ARCHITECTURE DESIGN
1.3 Mathematical models
(http://en.wikipedia.org/wiki/Swarm_behaviour)
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and understand the behavior.
simplest
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animal
swarm behaviour in birds, herding to refer to swarm behaviour in quadrupeds, shoaling or schooli
swarms generally represent individual animals as following three rules:
1.
Separation: Move in the same direction as your neighbors
2.
Alignment:IN
Remain
close to your neighbors
SWARM INTELLIGENCE
ARCHITECTURE
DESIGN
BOIDS
is an artificial
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developed by Craig Reynolds in 1986, which simulates the
3.
Cohesion: Avoid collisions
with your
neighbors
on this topic was published in 1987 in the proceedings of the ACM SIGGRAPH conference.The na
version of “bird-oid object”, which refers to a bird-like object
SWARM behaviour, or swarming, is a collective behaviour exhibited by animals of similar size which aggregate together, perhaps milling
about the same spot or perhaps moving en masse or migrating in some direction. As a term, swarming is applied particularly to insects,
but can also be applied to any other animal that exhibits swarm behaviour. The term flocking is usually used to refer specifically to
swarm behaviour in birds, herding to refer to swarm behaviour in quadrupeds, shoaling or schooling to refer to swarm behaviour in fish.
BOIDS is an artificial life
program, developed by Craig Reynolds in 1986, which simulates the flocking behaviour of birds. His paper
N ARCHITECTURE
DESIGN
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SWARM INTELLIGENCE (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept
is employed in work on artificial intelligence.
SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment.
The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no
centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between
such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents. Examples in natural systems of
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6
1.4 Boids System
( http://en.wikipedia.org/wiki/Boids )
Boids is an artificial life program, developed by Craig Reynolds in 1986, which
simulates the flocking behavior of birds. The name “boid” corresponds to a
shortened version of “bird-oid object”, which refers to a bird-like object. Its
pronunciation evokes that of “bird” in a stereotypical New York accent.
As with most artificial life simulations, Boids is an example of emergent behavior; that is, the complexity of Boids arises from the interaction of individual
agents (the boids, in this case) adhering to a set of simple rules. The rules applied in the simplest Boids world are the three basic rules of swarm behavior:
separation, alignment and cohesion.
At the paper, Flocks, Herds, and Schools: A Distributed Behavioral Model, it
says that” the aggregate motion of a flock of birds, a herd of land animals, or
a school of fish is a beautiful and familiar part of the natural world. But this
type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths
of each bird individually. The simulated flock is an elaboration of a particle
system, with the simulated birds being the particles. The aggregate motion of
the simulated flock is created by a distributed behavioral model much like that
at work in a natural flock; the birds choose their own course. Each simulated
bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that
rule its motion, and a set of behaviors programmed into it by the “animator.”
The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds.”
7
The model is based on simulating the behavior of each bird independently.
Working independently. The birds try both to stick together and avoid collisions
with one another and with other objects in their environment. The animations
showing simulated flocks built from this model seem to correspond to the
observer’s intuitive notion of what constitutes “flock-like motion. “The most
interesting motion of a simulated flock comes from interaction with other
objects in the environment. The isolated behavior of a flock tends to reach a
steady state and becomes rather sterile. The flock can be seen as a relaxation
solution to the constraints implied by its behaviors.
8
1.5 Swarm Intelligence
( http://en.wikipedia.org/wiki/Swarm_intelligence )
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing
SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often
comes from nature, especially biological systems. The agents follow very
simple rules, and although there is no centralized control structure dictating
how individual agents should behave, local, and to a certain degree random,
interactions between such agents lead to the emergence of “intelligent” global
behavior, unknown to the individual agents. Examples in natural systems of SI
include ant colonies, bird flocking, animal herding, bacterial growth, and fish
schooling.
The definition of swarm intelligence is still not quite clear. In principle, it
should be a multi-agent system that has self-organized behavior that shows
some intelligent behavior.
The application of swarm principles to robots is called swarm robotics, while
‘swarm intelligence’ refers to the more general set of algorithms. ‘Swarm
prediction’ has been used in the context of forecasting problems.
9
The examples of the algorithm include particle swarm optimization, ant colony
optimization, artificial bee colony algorithm, differential evolution, the bee’s
algorithm, artificial immune systems, grey wolf optimizer, bat algorithm, gravitational search algorithm, altruism algorithm, glowworm swarm optimization,
river formation dynamics, self-propelled particles, stochastic diffusion search
and multi-swarm optimization.
10
Chapter2: Swarm algorithms and application
11
2.1 Swarm Intelligence in Stadium Design
(http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703855/)
People in large-scale public areas are in danger because of a lot of manmade
or natural accidents, such as fire, hurricane, and bomb. For coping with these
emergencies, many scientists and engineers have paid much attention to the
researches about evacuation routes planning. In these researches, the application of swarm intelligence technologies to evacuation routes planning is a
hot topic because evacuation process itself is a collective behavior. Swarm
intelligence technology mainly includes particle swarm optimization (PSO)
technology and ant colony optimization (ACO) technology. The swarm intelligence technology is mainly used in two aspects: the simulation of evacuation
process and the optimization of evacuation routes. On one hand, swarm intelligence technologies have natural advantages to simulate collective behavior
such as evacuation process. On the other hand, the optimization mechanism of
swarm intelligence algorithms can effectively optimize evacuation objectives
by iterating the configuration of factors that affect evacuation efficiency. The
factors that affect evacuation efficiency includes pheromone, location of shelters in evacuation zone, the direction of lanes, the placement of road barriers,
and the scheduling of evacuation for each evacuee
12
Besides, evacuation routes optimization problem usually needs to consider
multiple objectives, such as total clearance time total number of survivals.
A few researches have involved the multi-objective evacuation routing optimization problem. Some of them applied swarm intelligence technologies to
solve this kind of problem
13
2.2 Swarm used at Design (by Tyler Julian Johnson)
(http://www.tyler-johnson.com/Swarm-Intelligence)
At the project by Tyler Julian Johnson, the role of agency within a generative
design process by using computational methodologies grounded in swarm
intelligence and casting a simple decision making ability into agents capable of self-organizing into an emergent intelligence. The projects focused on
technical code. Technical code writing required to cast swarm systems and
the architectural theory behind these systems. The project developed simulations of vector based swarm systems and then used these systems as a basis
for developing an architectural design methodology which operates within a
topological substrate. The second half of the project (moving from a two-dimensional environment to a three-dimensional environment) shifted away
from simply mapping agents over time and instead became a system capable
of negotiating architectural inputs.
Computational design is shifting away from heavy systems (like Maya’s MEL
scripting language) and into lighter weight object-oriented languages like
Processing and Rhinoscript. This entire simulation was written in Processing,
subdivided with a Rhinoscript, isosurfaced with Processing, and rendered with
V-Ray for Rhino.
14
15
16
17
2.3 Swarm Intelligence at Visualization (Robert Hodgin)
(http://roberthodgin.com/about/)
Robert Hodgin’s work ranges from simple 2D data visualizations to immersive
3D terrain simulations about swarm behavior. His primary interests include
theoretical physics, astronomy, particle engines, and audio visualizations. I
work in C++, Cinder, OpenGL, and GLSL.
Massive anchovy swarms off the coast of California have kept marine mammals and their observers busy for the past couple of months. It’s not so much
that there are more anchovy than usual, it’s that there are more anchovy
gathered in one place.
According to this article, anchovy movement can be due to a number of factors – plentiful plankton, mild temperatures – and this year, the anchovy stars
aligned over Monterey Bay. Their presence, telegraphed far and wide via whale
song, has set off a feeding frenzy of seals, whales, dolphins, and the press.
At the project, Magnetic Ink, it began as a tangent from the flocking studies.
The thinking was simple. What if the flocking birds rained down a fine mist
of ink onto a sheet of virtual paper? At the same time, they have ribbons that
hang from their feet and if they fly low enough, the ribbon will drag on the
paper and erase the ink.
18
The next project was flocking motion graphics for Nervo. What was required
was a couple videos of flocking using a 3D crow (or is it a raven) they would
provide. Simple enough. But given the tight deadline, the thought of doing a
render and posting it and waiting for approval or changes and then implementing the changes then retendering and reposting, etc… That process didn’t make
sense for this project so they decided to deliver them an application instead.
Using Processing, they started playing around with the flocking behavior to
make it more customizable. The original version of the flocking experiment
had very few controls and they had to be hard-coded. There was no run-time
adjustment. This was the first thing addressed. Several new parameters were
added. They included population density, gravity, drag, collision avoidance,
flight range, camera position and tracking, and a few toggles such as tethering
strings, floor plane, and bezier curves. Once the parameters were tweaked to
the user’s liking, they need only to hit the spacebar and an image sequence of
PNGs would start saving to the hard drive.
19
Once they had the exported image sequence, it was pretty easy to put it into a
post processing application and work his magic.
At the bait ball project, they have been experimenting with simulating group
dynamics for nearly as long as they have been coding. They became fascinated
with the work of Craig Reynolds who was among the first to show that with
very simple rulesets, predator and obstacle avoidance, and pursuit. Over a
decade later, I watched a talk by Professor Iain Couzin who is the head of the
Collective Animal Behavior department at Princeton. He explained the rules of
flocking behavior in a way that really clicked. That was the impetus to make
their first generative bait ball.
20
It was pretty easy to get these fish-objects to do something vaguely flocklike, but getting them to form into a torus proved to be a bit more of a challenge. If you just throw a bunch of flocking objects into a 3D environment,
often what happens is they come together into a clumped formation and then
they just head off as a group and disappear into the GL fog. He needed a way
to keep them corralled but without feeling like he was imposing unnatural
restrictions on their behavior. Every time he have to add a new controlling variable, he feel he am moving away from the purity of the behavior. But he didn’t
know what else to do so he introduced them to the concept of their own centroid. In addition to the flocking rules outlined by Reynolds, he added a general
desire to not wander too far away from the average of all of their positions.
This tiny change was all it required to get them to form stable and energetic
toroid’s.
21
2.4 Complexity of Swarm at Arts
(https://seeingcomplexity.wordpress.com)
Seems like swarm algorithms and flocking behavior are pretty popular these
days. The work of some architecture students, Hyun Chang Cho, Jun Ho Cho,
and Eun Ki Kang, all involved in some really cool visualizations.
Seems like swarm algorithms and flocking behavior are pretty popular these
days. The work of some architecture students, Hyun Chang Cho, Jun Ho Cho,
and Eun Ki Kang, all involved in some really cool visualizations.
22
Seems like swarm algorithms and flocking behavior are pretty popular these
days. The work of some architecture students, Hyun Chang Cho, Jun Ho Cho,
and Eun Ki Kang, all involved in some really cool visualizations.
23
There are more computational tools for swarmvis. An impressive tool (that
you can actually use yourself now; paper here), it is used for modeling flocking
behavior.
Originating in only a few simple rules, the program captures the idea that I
have been discussing for the past few days regarding emergent properties
of complex systems. As I argued in a previous post, what the human brain is
particularly good at is recognizing patterns. Once we find patterns in complex
systems, we can develop fairly simple rules that can explain these patterns (I
also suggested that this is a radical departure from contemporary statistical
methods, especially keeping in mind the prevalence of stochastic modelling).
Don Miner and Niels Kasch (site here), authors of swarmvis, work backward
to first form a set of simple rules that nonetheless produce quite complex
results.
24
2.5 Object to Field
( Stan Allen, From Object to Field : Field Conditions in Architecture and Urbanism)
Stan Allen’s ‘From object to Field’ clearly articulates an approach to heterogeneous space in architecture and urbanism that contrasts both Modernist ideals
of space as a uniform plane and Cubist concepts that informed Post-Modernist collage techniques. A series of historical case studies, such as Cordoba
Mosque and non-architectural referents, offers a summary genealogy of the
field within architecture and aesthetics in many ways, Allen’s use of the field
complements Robin Evan’s idea of the ‘matrix’, extends the implications of
Banham’s atmospheric architecture and can be understood as a translation
of Deleuze and Guattari’s presentation of ‘smooth space’ into the realm of
design. Allen’s presentation of ‘smooth space’ into the realm of design. Allen’s
presentation does more than offer the ‘field’ as another design trope, in that
field significantly alters the Modernist relationship between form, programme
and space, as well as blurring the normative boundary between the discrete
architectural building and larger urban forces and conditions. In that way, the
article implies a deterritorialisation of disciplinary striations of the environmental disciplines, such as architecture, landscape and city planning, moving
from the design of discrete artefacts to a choreography of multitudinous
relations.
Field conditions move from the one toward the many, from individuals to collectives, from objects to fields. A distinct but related set of meanings begins
with an intuition of a shift from object to field in recent theoretical and visual practices. In its most complex manifestation, ‘field conditions’ refers to
mathematical field theory, to non-linear dynamics and computer simulations
of evolutionary change. It parallels a shift in recent technologies from analog
object to digital field. It pays close attention to precedents in visual art, from
the abstract painting of 1920s to minimalist and post minimalist sculpture of
the 1960s.
In the late 1980s, artificial life theorist Craig Reynolds created a computer
program to simulate the flocking behavior of birds. Ad described by M Mitchell
Waldrop in Complexity: the Emerging science at the Edge of Order and Chaos,
Reynolds placed a large number of autonomous, bird-like agents, which he
called ‘boids’, into an onscreen environment. The boids were programed to follow three simple rules which were introduced at the beginning of this booklet.
The flock is clearly a field phenomenon, defined by precise and simple local
conditions, and relatively indifferent to overall form and extent. Because the
rules are defined locally, obstructions are not catastrophic to the whole.
25
Faculty of Architecture, University of Sao Paolo, Brazil
Variations and obstacles in the environment are accommodated by fluid adjustment. A small flock and a large flock desply fundamentally the same
structure. Over many interactions patterns emerge. Without repeating exactly,
flock behavior tends toward roughly similar configurations, not as fixed type,
but as cumulative result of localized behavior patterns.
Crowds present a different dynamic, motivated by more complex desires, and
interacting in less predictable pattern. Elias Canetti in Crowds and Power has
proposed a suggestive taxonomy: open and closed crowds; rhythmic and stagnating crowds; the slow crowd and the quick crowd. He examines the varieties
of the crowd, from religious throng formed by pilgrims, to the mass of participants in spectacle, even extending his thoughts to the following of rivers,
the piling up of crops and the density of the forest. According to Canetti, the
crowd has four primary attributes: the crowd always wants to grow; within a
crowd there is equality; the crowd loves density; the crowd needs a direction.
The relation to Reynolds’ rules outlined above is oblique, but visible. Canetti,
however, is not interested in predictionor verification. His sources are literary,
historical and personal. Moreover, he is always aware that the crowd can be
liberating as well as confining, angry and destructive as well as joyous.
26
Photograph by Oto Bihalji-Merin
Iannis Xenakis, Syrmos, 1959
27
In attempting to reproduce what he referred to as ‘global acoustical
events’, Xenakis drew upon his own considerable graphic imagination,
and his training in descriptive geometry to invert conventional procedures of composition. That is to say, he began with a graphic notation
describing the desired effect of ‘fields’ or ‘clouds’ of sound, and only
later reduced these graphics to conventional musical notation. Working
as he was with material that was beyond the order of magnitude of the
available compositional techniques, he had to invent new procedures
inorder to choreograph the ‘characteristic distribution of vast numbers
of events’.
Crowds and swarm s operate at the edge of control. Aside from the
suggestive formal possibilities, it was suggested with these two examples that architecture could profitably shift its attention from its
traditional top-down forms of control and begin to investigate the
possibilities of a more fluid, bottom-up approach. Field conditions offer
a tentative opening in architecture to address the dynamics of use,
behavior of crowds, swarm and the complex geometries of masses in
motion.
28
2.6 Swarm Tectonics
(Neil Leach , swarm tectonics: a manifesto for an emergent architecture)
In his book, Emergence: The Connected Lives of Ants, Cities and Software,
Steven Johnson presents the city as a manifestation of emergence. The city
operates as a dynamic, adaptive system, based on interactions with neighbors,
informational feedback loops, pattern recognition and indirect control. ‘Like
any emergent system’, notes Johnson, ‘the city is a pattern in time’. It displays
a bottom-up collective intelligence that is more sophisticated than the behavior of its parts.
The complex aerial choreography that unfolds through the motion of a flock of
birds exemplifies the emergence of collective behavior. Underlying the coherent elegance and fluidity of the flock is a highly sophisticated form of swarm
intelligence premised on the local interaction of individual agents that gives
rise
to a complex global behavior. The resultant order is not enforced from
above, but emerges from the bottom-up interaction of the agents leading to an
array of generative architectural design strategies.
29
30
2.7 Swarm Urbanism (Neil Leach)
There are a number of ways of modelling swarm intelligence within a computational framework. Manuel DeLanda outlines a model of agent-based behaviour that could be developed to understand the decision-making processes
within an actual city. These agents should be seen as concrete, singular individual agents, and not as abstract agents that embody the collective intelligence of an entire society. DeLanda’s research to date is based on institutional
organisations rather than urban forms of the city, and while he envisages the
possibility of a model which uses a system of intelligent agents capable of
making their own decisions and of influencing others in their decisions in order
to generate urban form in some way, he has yet to develop this model. The
term ‘swarm urbanism’ has been used fairly extensively within design circles.
Often this refers to a form of ‘swarm effect’, where a grid is morphed parametrically using either digital tools or Frei Otto’s ‘wet grid’ analogue technique. Such techniques, while producing interesting effects, are limited in that
31
they are either topologically fixed (as with a morphodynamic lattice) or base
geometrically fixed (as with the wet grid), and cannot make qualitative shifts
in form and space outside of these set-ups. The advantage of a genuinely bottom-up emergent system of swarm intelligence where individual agents with
embedded intelligence respond to one another is that it offers behavioural
translations of topology and geometry that can have radically varied outputs. One practice that does use swarm intelligence as a fully bottom-up
multi-agent design tool is Kokkugia, a network of young Australian architects
operating from New York and London. They have deployed this technique at
a macro level for a project in the Docklands in Melbourne, an urban redeve
opment currently under construction focusing on the extension of the Central
Business District into a disused port territory, and have extended it to a micro
level with the design of actual buildings, as with their Taipei Performing Arts
Centre. With their swarm urbanism projects, the concern of Kokkugia is not
to simulate actual populations (of people or institutions) or their occupation
of architecture, but to devise processes operating at much greater levels of
abstraction that involve seeding design intent into a set of autonomous design
agents which are capable of self-organising into emergent urban forms. They
are therefore not interested in mapping the motion of swarming agents to
generate an urban plan as a single optimal solution, but rather in developing
a flexible system embodying a collective self-organising urban intelligence:
‘An application of swarm logic to urbanism enables a shift from notions of
the master-plan to that of master-algorithm as an urban design tool. This
shift changes the conception of urban design from a sequential set of decisions at reducing scales, to a simultaneous process in which a set of micro
or local decisions interact to generate a complex urban system. Rather than
designing an urban plan that meets a finite set of criteria, urban imperatives
are programmed into a set of agents which are able to self-organise.’ This
approach tends to produce a result which – if not reducible to a single steadystate condition – will eventually coalesce into a nearequilibrium, semi-stable
state always teetering on the brink of disequilibrium. This allows the system
to remain responsive to changing economic, political and social circumstances. Kokkugia therefore sees the urban condition as one of constant flux: ‘Our
urban design methodology does not seek to find a single optimum solution but
rather a dynamically stable state that feeds off the instabilities of the relations that comprise it.’
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2.8 Swarm Modeling
(Paul Coates , The use of Swarm Intelligence to generate architectural form)
At the paper of swarm modeling, Carranza, Pablo Miranda and Coates, Paul
choose swarms as a study case is the fascination of the simplicity of its mechanics and its complexity as a phenomenon. It can be compared in that sense
with other models such as Cellular Automata, for example, with which shares
some similarities (they are parallel systems, they interact at a local level, etc).
It describes the swarms understanding them as examples of sensori-motor
intelligence. It begins addressing some issues already patent when studying
simple turtles, and then it looks at two ways of interaction of the swarm and
their implications. It studies the interaction with an environment in relation
with learning processes and simple perceptions of forms, and then uses the
processes developed in this first cases to look at the possibilities of interaction
of the swarm with a human, and its similarities with other systems such as
Genetic Algorithms or social systems.
33
2.9 Motion at Architecture Design
If motion in arts, montage, program and circulation could be translated into
an architecture design method, whether the ground motion like swarm behavior could also generate and architecture design method. Could the movement
space of nature could become an architecture space?
34
2.10 Particles at Architecture Design
(http://www.heatherwick.com/uk-pavilion/)
Particle elements are used at many architecture examples. Some of these
represent the motion pattern of nature movement or events.
At the UK pavilion, Shanghai Expo 2010, predicting that many of the Expo’s
pavilions might follow architectural trends in form-making, Heatherwick
Studio chosed instead to concentrate on exploring texture. They were thinking
of the opening sequence of the 1985 film Witness, in which the camera pans
across a field of grass swirled into patterns by the wind. On this windy riverside site, they wanted to make the building’s façade behave like this grass. It
also seemed that if you magnified the texture of a building enough, the texture
would actually become its form. For the future-gazing expo, seeds seemed an
ultimate symbol of potential and promise.
The Seed Cathedral is a box, 15 metres high and 10 metres tall. From every
surface protrude silvery hairs, consisting of 60,000 identical rods of clear
acrylic, 7.5 metres long, which extend through the walls of the box and lift it
into the air. Inside the pavilion, the geometry of the rods forms a space described by a curvaceous undulating surface. There are 250,000 seeds cast
into the glassy tips of all the hairs. By day, the pavilion’s interior is lit by the
sunlight that comes in along the length of each rod and lights up the seed
ends. You can track the daily movement of the sun and pick out the shadows
of passing clouds and birds and, when you move around, the light moves with
you, glowing most strongly from the hairs that point directly towards you. By
night, light sources inside each rod illuminate not only the seed ends inside the
structure, but the tips of the hairs outside it, covering the pavilion in tiny points
of light that dance and tingle in the breeze.
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Arne Quinze uses curves, lines, colours and movement in his pieces. his woodstick structures provide a feeling of movement and fluidity, that combine to
create a large frame structure.
(http://work-by-djg.blogspot.com/2011/02/artist-research-arne-quinze.html)
“Cities like open-air museums, sounds like realizing my ultimate dream; a confrontation with
the public surrounded by art every day. Art has a positive effect on human beings and their
personal development; it can extend their horizon and can broaden their view.”
____ Arne Quinze, Sint-Martens-Latem 2011
Quinze is known for his trademark sculptures made out of wooden planks.
His installations are built to provoke reaction and to intervene in the daily life
of passersby confronted with his sculptures. Quinze sees his installations as
places where people meet each other again and start conversations.
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In 2006, he gained a lot of attention by building Uchronia: A message from the
future, a large wide wooden sculpture at the Burning Man festival in Black Rock
City,in the Nevada desert, United States. Cityscape (2007) and The Sequence
(2008) are two of his giant wooden public art installations in the centre of Brussels, Belgium. It was the first time a sculpture gave the impression touching
two buildings in the city center while traffic still passes by underneath it. The
installation for the Flemish Parliament became an unequivocal actor in the city.
In Munich, Germany, he built Traveller (2008) for French luxury fashion and
leather goods brand Louis Vuitton. Other public art installations by Arne Quinze
have recently been revealed in the centre of Paris, France (Rebirth, 2008),Beirut,
Lebanon (The Visitor, 2009)and Louisville, Kentucky (Big Four Bridge, ongoing).
Arne Quinze: “With these sculptures I’m looking for a confrontation with the
public, I hope they start asking questions about what their function on this
planet is. What happens when putting all of the sudden an alien element in
the city, our habitual urban environment? How do we react to unusual objects
if we are confronted with them in our daily lives? Who or what remains the
stranger, the person confronted with it or the object itself?”
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GC Prostho Museum Research Center, by Kengo Kuma, is architecture that
originates from the system of Cidori, an old Japanese toy. Cidori is an assembly of wood sticks with joints having unique shape, which can be extended
merely by twisting the sticks, without any nails or metal fittings. The tradition
of this toy has been passed on in Hida Takayama, a small town in a mountain,
where many skilled craftsmen still exist.
The cement structure of the GC Prostho Museum Research Center has a rectangular floor plan on 3 levels surrounded by a parametric decorative system
formed of cypress wood elements generating regular prismatic combinations
created with interlocking joints.
This architecture shows the possibility of creating a universe by combining
small units like toys with your own hands. We worked on the project in the
hope that the era of machine-made architectures would be over, and human
beings would build them again by themselves.
(http://www.archdaily.com/199442/gc-prostho-museum-research-center-kengo-kuma-associates/)
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If the swarm behavior can be visually seen as some layers of architecture
skin, then patterns and layering might be a way of application in architecture
design for swarm simulation. There are something new and unseen in preexisting research on Japan about patterns and layering by Liotta and Matteo.
Salvator-John and Matteo have attempted to create a link between patterns
and layering. These two previously detached notions can now be integrated
into one methodology mediated by structural concepts that are the key to this
link. Structural analysis of the twentieth century struggled to advance beyond
the column and beam structural frame. Analysis today allows us to conceive
stable structures through the accumulation of delicate members, which have
the capacity to produce a variety of patterns while fulfilling their structural
responsibilities.
(Salvator-John A. LIOTT A and Matteo BELFIORE, Patterns and layering)
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Chapter3: Swarm Tectonics
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3.1 Swarm Testing
By using meatball and lines, swarm could be used as a tool of form finding.
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3.2 From Simulation to Application
Birds flocking and fish schooling were simulated by digital animation tool and
then replicated by different kind of particle elements. It was visually tested
for what kind of space could be resulted.
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45
Rather than static space constructed by huge concrete, glass, cubic frames,
the space formed by swarm might be constructed with more natural, various
sized materials and there are more potential for the skin which might be in
between solid, transparent, or translucent.
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3.3 Swarm structure
If swarm was seen as motion of particles, the connection by the particles or
the connection between different paths could construct as a structure-like
system.
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Tracking and connection are the two points to construct this kind of structure
system.
The space could be treated as a mobile zeppelin above the city or a pavilion
under the water.
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3.4 Skin attachment
Material characteristics of the skin decides the form.
There are two types of skin. One is frame with membrane, the other is panels
connection.
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3.5 Swarm Modular
A modular space could be seen as a cube controlled by 8 swarm points. Form
was changed based on the movement of the points.
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Simple xyz movement, rotation and extension, could generate complated
morph system.
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Swarm simulation and robot control could translate data into architecture
form.
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Connection enable the simple modular to construct various geometry.
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3.6 Swarm Joint
Rotation, extension and connection are three main typical mechanism design.
Rotation
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Connection
Extendsion
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Three type of joint could construct a swarm modular.
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Flexible material is casted into an organic shape outside of the mechanism
system. They work as a relationship like bone and muscle.
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3.7 Swarm Drawing
Swarm modular could generate a basic architecture frame. Particles around will
swarm based on simple principles to generate a form.
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If the path of these particles could be seen as a sketch drawing for architecture sketch, fibrous system could be used to simplify the complex lines into a
skin frame drawing.
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Attractor
Attracting region
Attractor path
Predator
Predator boundary
Bounding edge
Test No.01
Test No.02
Test No.03
Test No.04
Test No.05
Test No.06
Test No.07
Test No.08
Test No.09
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Swarm ParticleDrawings
No.01
No.04
No.07
Seek force : 172
Predator force : 251
Allignment : 251
Cohension : 1000
Seperation : 148
Seek force : 193
Predator force : 196
Allignment : 528
Cohension : 737
Seperation : 444
Seek force : 163
Predator force : 200
Allignment : 323
Cohension : 456
Seperation : 130
Seek force : 190
Predator force : 251
Allignment :251
Cohension : 1000
Seperation : 148
No.02
Seek force : 193
Predator force : 187
Allignment : 528
Cohension : 737
Seperation : 444
No.05
Seek force : 163
Predator force : 200
Allignment : 300
Cohension : 456
Seperation : 130
No.08
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No.03
No.06
No.09
Seek force : 90
Predator force : 251
Allignment : 251
Cohension : 1000
Seperation : 148
Seek force : 193
Predator force : 251
Allignment : 528
Cohension : 737
Seperation : 444
Seek force : 163
Predator force : 200
Allignment : 251
Cohension : 456
Seperation : 130
Reconstruct
No.01
No.02
No.03
No.04
No.05
No.06
No.07
No.08
No.09
Simplify
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3D modeling
No.01
No.02
No.03
No.04
No.05
No.06
No.07
No.08
No.09
Modeling
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Steel Sketch Model
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A
D
C
B
LEVE 3
LEVE 2
LEVE 1
2
1
D
1:100
A
C
B
Elevation
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1m
3m
6m
9m
3D Priint Frame Model
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3D Priint Frame Model
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3D Priint Skin Model
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3D Priint Skin Model
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Steel Sketch Model
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C
D
A
B
LEVE 6
LEVE 5
LEVE 4
LEVE 3
A
LEVE 2
LEVE 1
B
LEVE 0
1:50
C
D
Elevation Drawing
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1m
3m
6m
9m
3D Priint Model
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3D Priint Model
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3.8 Data Swarm
All particles active in groups and interactive with each other. For fish and
birds, the predator and attractor acts like a main force to effect the space
created by swarm. At the digital simulation, all points were set up with swarm
properties, effected by different kind of forces.
Top Urban area for startups
Startup Per Year, 1-5 (1995-2014)
Startup Per Year, 5-15 (1995-2014)
Startup Per Year, 15-250 (1995-2014)
Investment Flow Lines
Investee cities
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These forces could be wind, temperature, light, activities of human, program
of functions, or circulation. Therefore, we can translate the mapping of forces
into a kind of constrain for architectural swarm.
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The migration flows of startup investment acts like animals swarming in between cities. Events for startups happen around bay area with the increasing
of new companies. Through the simulation of these events, an urban swarm
footprint was discovered.
Redevelopment Agency District Office
Downtown Office
Downtown Commercial
Production, Distribution & Repair District
Neighborhood Comercial
Residential-Comercial Combined
Residential, Mixed (Houses & Appartments)
Residential, Houses
Residential, Houses
Open Space
Main Roads and Highway
Bicycle Network
Off Street Parking
Startupw with fundings
2014-2015 Startup Event Locations
Restaurants
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An architecture prototype based on swarm behavior and effected by the architectural forces will generate a new kind of incubator space for the startup
companies migrating above the cities.
Building height: 20-32 feet
Building height: 33-50 feet
Building height: 51-70 feet
Building height: 71-96 feet
Building height: 97-125 feet
Building height: 126-220 feet
Building height: 221-360 feet
Building height: 361-1000 feet
Open Space
Main Roads and Highway
Bicycle Network
Off Street Parking
Startupw with fundings
2014-2015 Startup Event Locations
Restaurants
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Total funding for starups (2004-2014)
Total funding for starups (2004-2014)
Investment flow simulation
Investment flow simulation
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Time frame 0
Time frame 3
Time frame 1
Time frame 4
Time frame 2
Time frame 5
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3.9Conclusion
Swarm could be generated into an architectural design process. Data constrain and the artificial selection are two of the key points. Data constrain represents a digital environment constructed by the data information from natural and social environment. Artificial selection is based on architects’ decision.
Different from mass production, there aren’t two identical form generated by
swarm, but the initial swarm principles and process are simple and similar.
Four main technology for swarm tectonics are mechanical robot control, 3d
printing, data collection and swarm simulation.
Swarm tectonics will increase the productivity and identity of architecture.
Data information will generate a digital environment as constrain for swarm
architectural behavior. Rather than designing a form, architects design a process to generate the form by swarm tectonics.
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Bibliography:
1. Steven Johnson, Emergence
2. Daniel Schodek, Dynamic digital representations in architecture visions in
motion
3. Smart Swarms – How Understanding Flocks, Schools and Colonies Can Make
Us Better at Communicating, Decision Making and Getting Things Done
4. Craig W. Reynolds, Flocks, Herds, and Schools: a Distributed Behavioral Model
5. Carranza, Pablo Miranda and Coates, Paul, Swarm modelling
6. Lebbeus Woods, Radical Reconstruction
7. Rem Koolhaas, Delirious New York: A Retroactive Manifesto for Manhattan
8. Sou Fujimoto , Primitive Future
9. Theodore Spyropoulos, John Frazer , Patrik Schumacher, Adaptive Ecologies
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