Generative Design in Civil Engineering
Using Cellular Automata
Rafal Kicinger
June 16, 2006
Outline
• Generative Design
• Cellular Automata as Design
Generators
– Steel Structures in Tall Buildings
– Traffic Control Systems in Urban Areas
•
•
•
•
Emergent Designer
Design Experiments
Experimental Results
Conclusions
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Generative Design: Representation
• Design representations
– One of the key aspects of any computational
design activity
– Describe design’s form, its components, etc.
– Incorporate domain-specific knowledge
– Determine the space in which solutions are
sought
• Need to address important engineering
objectives
– Novelty
– Optimization
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Traditional Design Representations
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Generative Design
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Generative Design
• Cellular automata generating designs
– Steel structural systems in tall buildings
– Traffic control system in urban areas
• Evolutionary algorithms searching the
spaces of generative representations
(design embryos + design rules)
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Cellular Automata as Design Generators
Steel Structural Systems in Tall Buildings
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Cellular Automata as Design Generators
Traffic Control Systems in Urban Areas
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Cellular Automata as Design Generators
Traffic Control Systems in Urban Areas
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Emergent Designer
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Emergent Designer
System architecture
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Design Experiments
Extensive Computational Experiments
Conducted
– Steel Structural Systems in Tall Buildings
• Exhaustive search of all elementary CAs started from
arbitrary and randomly generated design embryos
• Generative representations based on 1D CAs evolved
using evolutionary algorithms
– Traffic Control Systems in Urban Areas
• Generative representations based on 2D CAs evolved
using evolutionary algorithms
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Design Experiments
• Steel structural
systems:
– number of bays - 5
– number of stories - 30
– bay width - 20 feet
– story height - 14 feet
• Arbitrary design
embryos used:
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Design Experiments
Traffic Control Systems
– Number of network
nodes 65
– Number of network
links 80
– Number of traffic
signals 25
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Design Experiments
• CA representation parameters:
–
–
–
–
–
CA dimension:
1D and 2D
CA neighborhood radius:
1 and 2
number of cell state values:
2 and 7
CA neighborhood shape (2D CAs): Moore
CA iteration steps (2D CAs):
14
• Evolutionary computation parameters:
– evolutionary algorithm: ES
– population sizes (parent, offspring): (1,5),
(5,25),(5,125)
– mutation rate: 0.025, 0.05, 0.1, 0.3
– crossover (type, rate): uniform, 0.2
– fitness: weight of the steel skeleton structure,
or the total vehicle time
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Experimental Results
• Exhaustive Search: Arbitrary Design Embryos
Best designs:
Total weight:
Max. displacement:
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Experimental Results
Distributions plotted with respect to two
objectives:
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Experimental Results
Exhaustive Search: Random Design Embryos
Simple X bracings
K bracings
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Experimental Results
Evolutionary search of generative
representations: steel structures
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Experimental Results
Evolutionary search of generative
representations: traffic control systems
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Conclusions
• Generative representations based on
cellular automata proved to perform
well for civil engineering problems
where some regularity/patterns are
expected, or desired
• They produced quantitatively better
solutions (6-20% average
performance improvement) than
traditional design representations
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Conclusions
• CA representations produced
qualitatively different patterns than
patterns obtained using traditional
representations
• They can be efficiently optimized
by evolutionary algorithms,
particularly in the case of 1D CA
representations
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Credits
• The work on generative design of steel
structural systems in tall buildings was
conducted together with Drs. Tomasz
Arciszewski and Kenneth De Jong
• The work on generative design of traffic
control systems in urban areas was
conducted with Dr. Michael Bronzini
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Backup Slides
• Evolutionary search of elementary
CAs: K bracings
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Backup Slides
• Evolutionary search of elementary
CAs: Simple X bracings
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