generative design in civil engineering using cellular automata rafal kicinger june 16, 2006
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Generative Design in Civil Engineering Using Cellular Automata
Rafal KicingerJune 16, 2006
NKS 2006, June 16-18, 2006, Washington, DC 2
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 DesignerSystem 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 ResultsExhaustive Search: Random Design Embryos
Simple X bracings K bracings
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Experimental ResultsEvolutionary search of generative representations: steel structures
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Experimental ResultsEvolutionary 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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