Citation

J. Highway Transp. Res. Dev. (English ed.) 2019; 13(1): 94-103.

Copyright

(Copyright © 2019, Research Institute of Highway, Ministry of Transport in association with the American Society of Civil Engineers)

DOI

10.1061/JHTRCQ.0000671

PMID

unavailable

Abstract

An approximate model can be used to replace the real model to reduce the computing time and guarantee the feasibility of optimization. However, the approximate model must meet the required accuracy. The higher the accuracy of the approximate model, the higher the reliability of optimization results. In this study, a finite element model for the 40% offset impact of minibuses was established, with the thickness of 10 plates in the front of the car body as the design variable and peak acceleration of B pillar lower end, the total mass, and the intrusion volume of dashboard beam, steering column hole, and clutch pedal as the response values. We obtained 70 sample points by the Latin hypercube experimental design method and built approximate models of design variables and the response. Then we compared the relative error scatter, mean relative error, and decision co-efficient of the response surface approximate model, radial basis function network approximate model, kriging approximate model, and orthogonal polynomial approximate model. Results show that the prediction accuracy of the response surface approximate model and radial basis function network approximate model in the peak acceleration of B pillar lower end and the intrusion volume of the steering column hole and clutch pedal does not meet the requirement. The orthogonal polynomial approximate model has a high prediction accuracy in total mass, but its prediction accuracy in other responses does not meet the requirement. In addition, all three approximate models are obviously influenced by the linear relationship between the response and the variables. The kriging approximate model meets the required prediction accuracy of the four responses and is less affected by the linear relationship. Thus, the kriging approximate model is suitable to replace the original model. Then particle swarm optimization can be performed to optimize the kriging approximate model. This study shows that the kriging approximate model has high fitting precision, and the optimization results reach the expected aim.

Language: en