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Optimal Design of Building Structures Using Genetic Algorithms


Chan, Eduardo (1997) Optimal Design of Building Structures Using Genetic Algorithms. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/4jea-yw44.


A general framework for multi-criteria optimal design is presented which is well-suited for automated design of structural systems. A systematic computer-aided optimal design decision process is developed which allows the designer to rapidly evaluate and improve a proposed design by taking into account the major factors of interest related to different aspects such as design, construction, and operation.

The proposed optimal design process requires the selection of the most promising choice of design parameters taken from a large design space, based on an evaluation using specified criteria. The design parameters specify a particular design, and so they relate to member sizes, structural configuration, etc. The evaluation of the design uses performance parameters which may include structural response parameters, risks due to uncertain loads and modeling errors, construction and operating costs, etc. Preference functions are used to implement the design criteria in a "soft" form. These preference functions give a measure of the degree of satisfaction of each design criterion. The overall evaluation measure for a design is built up from the individual measures for each criterion through a preference combination rule. The goal of the optimal design process is to obtain a design that has the highest overall evaluation measure - an optimization problem.

Genetic algorithms are stochastic optimization methods that are based on evolutionary theory. They provide the exploration power necessary to explore high-dimensional search spaces to seek these optimal solutions. Two special genetic algorithms, hGA and vGA, are presented here for continuous and discrete optimization problems, respectively.

The methodology is demonstrated with several examples involving the design of truss and frame systems. These examples are solved by using the proposed hGA and vGA.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Structural optimization ; Genetic algorithms ; Combinatorial optimization
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Civil Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Beck, James L.
Group:Earthquake Engineering Research Laboratory
Thesis Committee:
  • Unknown, Unknown
Defense Date:10 June 1997
Other Numbering System:
Other Numbering System NameOther Numbering System ID
Funding AgencyGrant Number
Kajima-Curee Joint Research ProgramUNSPECIFIED
Earthquiake Research Affiliate Program of CaltechUNSPECIFIED
Record Number:CaltechThesis:03132014-152131012
Persistent URL:
Related URLs:
URLURL TypeDescription DocumentTechnical Report EERL 97-06 in CaltechAUTHORS
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8132
Deposited By: Kathy Johnson
Deposited On:13 Mar 2014 22:39
Last Modified:13 Aug 2021 21:22

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