An Evolutionary Based Methodology for Representing and Evolving Structural Design Solutions
Raich, Anne Marie
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Permalink
https://hdl.handle.net/2142/83473
Description
Title
An Evolutionary Based Methodology for Representing and Evolving Structural Design Solutions
Author(s)
Raich, Anne Marie
Issue Date
1999
Doctoral Committee Chair(s)
Ghaboussi, Jamshid
Department of Study
Civil and Environmental Engineering
Discipline
Civil and Environmental Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, System Science
Language
eng
Abstract
Performing synthesis during conceptual design provides substantial cost savings by selecting the structural topology and geometry of the design, in addition to selecting the member sizes. Traditional optimization methods, however, cannot synthesize design solutions that have diverse structural topologies and geometries. A new evolutionary based search method was developed to support the synthesis of design solutions in unstructured problem domains. The implicit redundant representation genetic algorithm (IRR GA) used redundancy to support the self-organization of the representation by encoding a variable number of location independent design parameters. Using an unstructured definition of the problem domain allowed the definition and evaluation of diverse structures that had variable topologies and geometries. The performance of IRR GA was better than a simple GA and a structured GA experimentally. The IRR GA provided several benefits: redundant segments protected existing variables from the disruption of crossover and mutation; new variables could be designated within previously redundant segments; and the number of parameters represented dynamically changed the dimensions of the search space. The adaptability of the IRR GA was evaluated by transforming the design environment. The transformations were required to model the topology and geometry changes that occur during synthesis. Experimental results showed that the IRR GA could adapt to environmental transformations without detrimentally impacting performance. The IRR GA was applied to evolve synthesis design solutions for an unstructured, multi-objective frame problem domain. Several levels of unstructured formulations were examined. The difficulties of evaluating the topology and geometry dependent loading configurations, penalty functions, and multiple objectives were analyzed. The IRR GA did not require the definition of a ground structure or heuristic rules to add or remove structural elements. The IRR GA synthesis design method generated novel frame designs that compared favorably with solutions obtained using a trial and error design process.
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