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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
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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 NKS 2006, June 16-18, 2006, Washington, DC
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 NKS 2006, June 16-18, 2006, Washington, DC
Traditional Design Representations NKS 2006, June 16-18, 2006, Washington, DC
Generative Design NKS 2006, June 16-18, 2006, Washington, DC
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) NKS 2006, June 16-18, 2006, Washington, DC
Cellular Automata as Design Generators Steel Structural Systems in Tall Buildings NKS 2006, June 16-18, 2006, Washington, DC
Cellular Automata as Design Generators Traffic Control Systems in Urban Areas NKS 2006, June 16-18, 2006, Washington, DC
Cellular Automata as Design Generators Traffic Control Systems in Urban Areas NKS 2006, June 16-18, 2006, Washington, DC
Emergent Designer NKS 2006, June 16-18, 2006, Washington, DC
Emergent Designer System architecture NKS 2006, June 16-18, 2006, Washington, DC
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 NKS 2006, June 16-18, 2006, Washington, DC
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: NKS 2006, June 16-18, 2006, Washington, DC
Design Experiments Traffic Control Systems • Number of network nodes - 65 • Number of network links - 80 • Number of traffic signals - 25 NKS 2006, June 16-18, 2006, Washington, DC
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 NKS 2006, June 16-18, 2006, Washington, DC
Experimental Results • Exhaustive Search: Arbitrary Design Embryos Best designs: Total weight: Max. displacement: NKS 2006, June 16-18, 2006, Washington, DC
Experimental Results Distributions plotted with respect to two objectives: NKS 2006, June 16-18, 2006, Washington, DC
Experimental Results Simple X bracings K bracings Exhaustive Search: Random Design Embryos NKS 2006, June 16-18, 2006, Washington, DC
Experimental Results Evolutionary search of generative representations: steel structures NKS 2006, June 16-18, 2006, Washington, DC
Experimental Results Evolutionary search of generative representations: traffic control systems NKS 2006, June 16-18, 2006, Washington, DC
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 NKS 2006, June 16-18, 2006, Washington, DC
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 NKS 2006, June 16-18, 2006, Washington, DC
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 NKS 2006, June 16-18, 2006, Washington, DC
Backup Slides • Evolutionary search of elementary CAs: K bracings NKS 2006, June 16-18, 2006, Washington, DC
Backup Slides • Evolutionary search of elementary CAs: Simple X bracings NKS 2006, June 16-18, 2006, Washington, DC