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Vehicle Crashworthiness Design Via a Surrogate Model Ensemble and a Co-Evolutionary Genetic Algorithm

[+] Author Affiliations
Karim Hamza, Kazuhiro Saitou

University of Michigan, Ann Arbor, MI

Paper No. DETC2005-84965, pp. 899-907; 9 pages
  • ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 31st Design Automation Conference, Parts A and B
  • Long Beach, California, USA, September 24–28, 2005
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4739-X | eISBN: 0-7918-3766-1
  • Copyright © 2005 by ASME


This paper presents a new method for designing vehicle structures for crashworthiness using surrogate models and a genetic algorithm. Inspired by the classifier ensemble approaches in pattern recognition, the method estimates the crash performance of a candidate design based on an ensemble of surrogate models constructed from the different sets of samples of finite element analyses. Multiple sub-populations of candidate designs are evolved, in a co-evolutionary fashion, to minimize the different aggregates of the outputs of the surrogate models in the ensemble, as well as the raw output of each surrogate. With the same sample size of finite element analyses, it is expected the method can provide wider ranges potentially high-performance designs than the conventional methods that employ a single surrogate model, by effectively compensating the errors associated with individual surrogate models. Two case studies on simplified and full vehicle models subject to full-overlap frontal crash conditions are presented for demonstration.

Copyright © 2005 by ASME



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