Journal of Shanghai Jiaotong University ›› 2020, Vol. 54 ›› Issue (6): 652-660.doi: 10.16183/j.cnki.jsjtu.2018.270

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A Hybrid Evolutionary Algorithm for Identifying Multiple Alternatives in Model Updating

KANG Juntao,ZHANG Yazhou,QIN Shiqiang   

  1. School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
  • Online:2020-06-28 Published:2020-07-03

Abstract: To make the result of finite element model updating more accord with the real structure, this paper converts to provide multiple alternatives, instead of offering just a single result. With those options, the policy makers can apply their working experience and consider field condition, which leads to more suitable decision. This paper proposes a hyper algorithm which combines the steady-state genetic algorithm and the gradient descent algorithm. A numerical simulation and an ASCE-Benchmark problem are employed to verify the ability to find multiple alternatives and optimization accuracy of the proposed method. The results show that the algorithm can optimize all the minimums and show a better accuracy compared to the steady-state genetic algorithm. After updating the frequency, error between the finite element model and real structure is significantly reduced.

Key words: structural health monitoring, finite element model updating, multiple alternatives, steady-state genetic algorithm, gradient descent algorithm

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