Multiobjective optimization for crash safety design of vehicles using stepwise regression model

  • Xingtao Liao
  • Qing Li
  • Xujing Yang
  • Weigang Zhang
  • Wei Li
Industrial Application


In automotive industry, structural optimization for crashworthiness criteria is of special importance. Due to the high nonlinearities, however, there exists substantial difficulty to obtain accurate continuum or discrete sensitivities. For this reason, metamodel or surrogate model methods have been extensively employed in vehicle design with industry interest. This paper presents a multiobjective optimization procedure for the vehicle design, where the weight, acceleration characteristics and toe-board intrusion are considered as the design objectives. The response surface method with linear and quadratic basis functions is employed to formulate these objectives, in which optimal Latin hypercube sampling and stepwise regression techniques are implemented. In this study, a nondominated sorting genetic algorithm is employed to search for Pareto solution to a full-scale vehicle design problem that undergoes both the full frontal and 40% offset-frontal crashes. The results demonstrate the capability and potential of this procedure in solving the crashworthiness design of vehicles.


Crashworthiness Multiobjective optimization Stepwise regression Finite element method Genetic algorithm 


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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Xingtao Liao
    • 1
  • Qing Li
    • 2
  • Xujing Yang
    • 1
  • Weigang Zhang
    • 1
  • Wei Li
    • 2
  1. 1.State Key Laboratory of Advanced Design and Manufacture for Vehicle BodyHunan UniversityChangshaChina
  2. 2.School of Aerospace, Mechanical and Mechatronic EngineeringThe University of SydneySydneyAustralia

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