Volume 24 Issue 2
Apr.  2024
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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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    • References(31)
  • [1]
    GOULET J A, KOO K. Empirical validation of Bayesian dynamic linear models in the context of structural health monitoring[J]. Journal of Bridge Engineering, 2018, 23(2): 05017017. doi: 10.1061/(ASCE)BE.1943-5592.0001190
    [2]
    KAMARIOTIS A, CHATZI E, STRAUB D. Value of information from vibration-based structural health monitoring extracted via Bayesian model updating[J]. Mechanical Systems and Signal Processing, 2022, 166: 108465. doi: 10.1016/j.ymssp.2021.108465
    [3]
    WANG Zuo-cai, DING Ya-jie, GE Bi, et al. Review on nonlinear model updating for bridge structures[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 59-75. (in Chinese). doi: 10.19818/j.cnki.1671-1637.2022.02.004
    [4]
    SOHN H, LAW K H. A Bayesian probabilistic approach for structure damage detection[J]. Earthquake Engineering and Structural Dynamics, 1997, 26(12): 1259-1281. doi: 10.1002/(SICI)1096-9845(199712)26:12<1259::AID-EQE709>3.0.CO;2-3
    [5]
    FANG Chang-yu, ZHANG Yao-ting. Model updating of prestressed concrete beams using parameters uncertainty[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2011, 39(11): 87-91. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG201111019.htm
    [6]
    BECK J L, KATAFYGIOTIS L S. Updating models and their uncertainties. Ⅰ: Bayesian statistical framework[J]. Journal of Engineering Mechanics, 1998, 124(4): 455-461. doi: 10.1061/(ASCE)0733-9399(1998)124:4(455)
    [7]
    BEHMANESH I, MOAVENI B. Probabilistic identification of simulated damage on the Dowling Hall Footbridge through Bayesian finite element model updating[J]. Structural Control and Health Monitoring, 2015, 22(3): 463-483. doi: 10.1002/stc.1684
    [8]
    LAM H F, YANG Jia-hua, AU S K. Bayesian model updating of a coupled-slab system using field test data utilizing an enhanced Markov chain Monte Carlo simulation algorithm[J]. Engineering Structures, 2015, 102: 144-155. doi: 10.1016/j.engstruct.2015.08.005
    [9]
    YANG Jia-hua, LAM H F, BECK J L. Bayes-Mode-ID: a Bayesian modal-component-sampling method for operational modal analysis[J]. Engineering Structures, 2019, 189: 222-240. doi: 10.1016/j.engstruct.2019.03.047
    [10]
    LIU Gang, LUO Jun, QIN Yang, et al. A finite element model updating method based on improved MCMC method[J]. Engineering Mechanics, 2016, 33(6): 138-145. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201606018.htm
    [11]
    WAN Hua-ping, REN Wei-xin. Stochastic model updating utilizing Bayesian approach and Gaussian process model[J]. Mechanical Systems and Signal Processing, 2016, 70/71: 245-268. doi: 10.1016/j.ymssp.2015.08.011
    [12]
    LIU Zeng-yu, YANG Jia-hua, LAM H F, et al. A Markov chain Monte Carlo-based Bayesian framework for system identification and uncertainty estimation of full-scale structures[J]. Engineering Structures, 2023, 295: 116886. doi: 10.1016/j.engstruct.2023.116886
    [13]
    SONG Yan-peng, CHEN Hui, HUANG Bin. Finite element model updating method of reinforced concrete beam based on Bayesian method[J]. Journal of Civil Engineering and Management, 2019, 36(4): 126-132, 139. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WHCJ201904020.htm
    [14]
    WANG Xiao-you, HOU Rong-rong, XIA Yang, et al. Structural damage detection based on variational Bayesian inference and delayed rejection adaptive Metropolis algorithm[J]. Structural Health Monitoring, 2021, 20(4): 1-18.
    [15]
    REN Wei-xin, DE ROECK G. Structural damage identification using modal data. Ⅱ: test verification[J]. Journal of Structural Engineering, 2002, 128(1): 96-104. doi: 10.1061/(ASCE)0733-9445(2002)128:1(96)
    [16]
    MALEKGHAINI N, GHAHARI F, EBRAHIMIAN H, et al. Time-domain finite element model updating for operational monitoring and damage identification of bridges[J]. Structural Control and Health Monitoring, 2023, 2023: 4170149.
    [17]
    PENG Zhen-rui, ZHENG Jie, BAI Yu, et al. A model updating method based on an improved MCMC algorithm[J]. Journal of Vibration and Shock, 2020, 39(4): 236-245. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202004031.htm
    [18]
    WU Ling-zi, JI Wen-ying, ABOURIZKS M. Bayesian inference with Markov chain Monte Carlo-based numerical approach for input model updating[J]. Journal of Computing in Civil Engineering, 2020, 34(1): 04019043. doi: 10.1061/(ASCE)CP.1943-5487.0000862
    [19]
    JIANG Shou-yan, ZHAO Lin-xin, DU Cheng-bin. Identification of multiple flaws in structures based on frequency and modal assurance criteria[J]. Chinese Journal of Theoretical and Applied Mechanics, 2019, 51(4): 1091-1100. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-LXXB201904012.htm
    [20]
    WENG Shun, ZHU Hong-ping. Damage identification of civil structures based on finite element model updating[J]. Engineering Mechanics, 2021, 38(3): 1-16. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX202103002.htm
    [21]
    YI Wei-jian, ZHOU Yun, LI Hao. Damage assessment research on frame structure based on Bayesian statistical inference[J]. Engineering Mechanics, 2009, 26(5): 121-129. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX200905022.htm
    [22]
    YUEN K V. Recent developments of Bayesian model class selection and applications in civil engineering[J]. Structural Safety, 2010, 32(5): 338-346. doi: 10.1016/j.strusafe.2010.03.011
    [23]
    CHEUNG S H, BECK J L. Bayesian model updating using hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters[J]. Journal of Engineering Mechanics, 2009, 135(4): 243-255. doi: 10.1061/(ASCE)0733-9399(2009)135:4(243)
    [24]
    SHERRI M, BOULKAIBET I, MARWALA T, et al. Bayesian finite element model updating using a population Markov chain Monte Carlo algorithm[J]. Special Topics in Structural Dynamics and Experimental Techniques, 2021, 5: 259-269.
    [25]
    HAO Yan-ling, SHAN Zhi-ming, SHEN Feng. Parameter estimation of α-stable distributions using DRAM algorithm[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2011, 39(10): 73-78. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG201110019.htm
    [26]
    SHERRI M, BOULKAIBET I, MARWALA T, et al. Bayesian finite element model updating using an improved evolution Markov chain algorithm[J]. Model Validation and Uncertainty Quantification, 2022, 3: 163-174.
    [27]
    WENTWORTH M T, SMITH R C, WILLIAMS B. Bayesian model calibration and uncertainty quantification for an HIV model using adaptive metropolis algorithms[J]. Inverse Problems in Science and Engineering, 2018, 26(2): 233-256. doi: 10.1080/17415977.2017.1312365
    [28]
    TERBRAAK C J F T. A Markov chain Monte Carlo version of the genetic algorithm differential evolution: easy Bayesian computing for real parameter spaces[J]. Statistics and Computing, 2006, 16(3): 239-249. doi: 10.1007/s11222-006-8769-1
    [29]
    WU Shen-hua, LEI Xiao-yan. Convergence condition of cross iterative algorithm for vehicle-track nonlinear coupling equations[J]. Journal of East China Jiaotong University, 2015, 32(3): 23-31. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HDJT201503005.htm
    [30]
    SUN Lu, DUAN Yu-fen, GAO Pei-pei. Comparison of structural analysis models for slab ballastless track in high-speed railway[J]. Journal of Southeast University (Natural Science Edition), 2013, 43(5): 938-943. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201305007.htm
    [31]
    YE Ling, CHEN Hua-peng, LIU Chang-yu. Parameter identification of rail transit infrastructure based on substructure method[J]. Journal of the China Railway Society, 2022, 44(5): 91-100. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202205012.htm
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