Back to Search Start Over

Nonlinear Optimization of Orthotropic Steel Deck System Based on Response Surface Methodology

Authors :
Ya Wei
Minshan Pei
Xiaodong Liu
Chuang Yan
Wei Huang
Source :
Research, Research, Vol 2020 (2020)
Publication Year :
2020
Publisher :
American Association for the Advancement of Science (AAAS), 2020.

Abstract

The steel bridge deck system, directly subjected to the vehicle load, is an important component to be considered in the optimization design of the bridges. Due to its complex structure, the design parameters are coupled with each other, and many fatigue details in the system result in time-consuming calculation during structure optimization. In view of this, a nonlinear optimization method based on the response surface methodology (RSM) is proposed in this study to simplify the design process and to reduce the amount of calculations during optimization. The optimization design of the steel bridge deck system with two-layer pavement on the top of the steel deck plate is taken as an example, the influence of eight structural parameters is considered. The Box-Behnken design is used to construct a sample space in which the eight structural parameters can be distributed evenly to reduce the calculation workload. The finite element method is used to model the mechanical responses of the steel bridge deck system. From the regression analysis by the RSM, the explicit relationships between the fatigue details and the design parameters can be obtained, based on which the nonlinear optimization design of the bridge deck system is conducted. The influence of constraint functions, objective functions, and optimization algorithms is also analyzed. The method proposed in this study is capable of considering the influence of different structural parameters and different optimization objectives according to the actual needs, which will effectively simplify the optimization design of the steel bridge deck system.

Details

ISSN :
26395274
Volume :
2020
Database :
OpenAIRE
Journal :
Research
Accession number :
edsair.doi.dedup.....f183316fff3133598808e5c90f5249b3
Full Text :
https://doi.org/10.34133/2020/1303672