Generative Design in Civil Engineering Using Cellular Automata

Rafal KicingerJune 16, 2006

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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