Volume 24 Issue 2
Apr.  2024
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2024  >  24(2): 112-124
YE Ling, JIANG Hong-kang, CHEN Hua-peng, FENG Yu-xuan, WANG Li-cheng. Finite element model correction for ballastless track structure based on multi-chain competition based differential evolution algorithm[J]. Journal of Traffic and Transportation Engineering, 2024, 24(2): 112-124. doi: 10.19818/j.cnki.1671-1637.2024.02.007
Citation: YE Ling, JIANG Hong-kang, CHEN Hua-peng, FENG Yu-xuan, WANG Li-cheng. Finite element model correction for ballastless track structure based on multi-chain competition based differential evolution algorithm[J]. Journal of Traffic and Transportation Engineering, 2024, 24(2): 112-124. doi: 10.19818/j.cnki.1671-1637.2024.02.007
shu

Finite element model correction for ballastless track structure based on multi-chain competition based differential evolution algorithm

doi: 10.19818/j.cnki.1671-1637.2024.02.007
  • 1.

    State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, Jiangxi, China

  • 2.

    Jiangxi Transport Investment Consulting Group Co., Ltd., Nanchang 330108, Jiangxi, China

Funds:

National Natural Science Foundation of China 52008168

More Information
  • Author Bio:

    YE Ling(1989-), female, assistant professor, PhD, 58718070@qq.com

  • Received Date: 2023-11-08
    Available Online: 2024-05-16
  • Publish Date: 2024-04-30
    Abstract
  • To obtain a more realistic track structure model, a finite element model correction method for the ballastless track structure based on a multi-chain competition based differential evolution algorithm was proposed. The objective function and likelihood function suitable for the ballastless track structure were established according to the response of frequency vibration mode. Based on the standard Markov Chain Monte Carlo algorithm, a multi-chain differential evolution algorithm was introduced to solve the problems of low efficiency and difficult convergence in high-dimensional parameter models. Then, the competitive algorithm was introduced, and the mechanism of learning from the winners by the losers determined by the competition was utilized, so as to continuously iteratively correct the track model and thus improve the correction accuracy. On this basis, the efficiency of the proposed method was verified by a numerical example of the finite element model correction of the ballastless track structure. Analysis results show that after correction using the Metropolis-Hastings algorithm and the delayed rejection adaptive Metropolis algorithm, the maximum relative errors between the corrected unit parameters and the true values are 4.75% and 1.35%, respectively. However, the maximum relative error between the corrected unit parameters and the true values is 0.28%, after correction using the multi-chain competition based differential evolution algorithm. In addition, the correlation between vibration mode vectors is close to 1, indicating that the correction accuracy of the multi-chain competition based differential evolution algorithm is better than the other two algorithms. In the noise tests with 5%, 10%, and 15% noise, respectively, the parameter errors reach about 9% after corrected by the Metropolis-Hastings algorithm and the delayed rejection adaptive Metropolis algorithm, while those corrected by the multi-chain competition based differential evolution algorithm are all within 5%. It further proves the good robustness of the multi-chain competition based differential evolution algorithm. Therefore, the multi-chain competition based differential evolution algorithm can provide a new method for correcting the finite element model of ballastless tracks with incomplete testing information caused by complex environments.

     

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