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Damage Detection of High-Speed Railway Box Girder Using Train-Induced Dynamic Responses

Author

Listed:
  • Xin Wang

    (School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, China)

  • Yi Zhuo

    (China Railway Design Corporation, Tianjin 300142, China)

  • Shunlong Li

    (School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, China)

Abstract

This paper proposes a damage detection method based on the train-induced responses of high-speed railway box girders. Under the coupling effects of bending and torsion, the traditional damage detection method based on the Euler beam theory cannot be applied. In this research, the box girder section is divided into different components based on the plate element analysis method. The strain responses were preprocessed based on the principal component analysis (PCA) method to remove the influence of train operation variation. The residual error of the autoregressive (AR) model was used as a potential index of damage features. The optimal order of the model was determined based on the Bayesian information criterion (BIC) criterion. Finally, the confidence boundary (CB) of damage features (DF) constituting outliers can be estimated by the Gaussian inverse cumulative distribution function (ICDF). The numerical simulation results show that the proposed method in this paper can effectively identify, locate and quantify the damage, which verifies the accuracy of the proposed method. The proposed method effectively identifies the early damage of all components on the key section by using four strain sensors, and it is helpful for developing effective maintenance strategies for high-speed railway box girders.

Suggested Citation

  • Xin Wang & Yi Zhuo & Shunlong Li, 2023. "Damage Detection of High-Speed Railway Box Girder Using Train-Induced Dynamic Responses," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8552-:d:1155086
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    References listed on IDEAS

    as
    1. Xin He & Guojin Tan & Wenchao Chu & Sufeng Zhang & Xueliang Wei, 2022. "Reliability Assessment Method for Simply Supported Bridge Based on Structural Health Monitoring of Frequency with Temperature and Humidity Effect Eliminated," Sustainability, MDPI, vol. 14(15), pages 1-23, August.
    2. Datteo, Alessio & Busca, Giorgio & Quattromani, Gianluca & Cigada, Alfredo, 2018. "On the use of AR models for SHM: A global sensitivity and uncertainty analysis framework," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 99-115.
    3. Shaoyi Zhang & Yongliang Wang & Kaiping Yu, 2022. "Steady-State Data Baseline Model for Nonstationary Monitoring Data of Urban Girder Bridges," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    4. Zhonglong Li & Wei Ji & Yao Zhang & Sijia Ge & Haonan Bing & Mingjun Zhang & Zhifeng Ye & Baowei Lv, 2022. "Study on the Reliability Evaluation Method and Diagnosis of Bridges in Cold Regions Based on the Theory of MCS and Bayesian Networks," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    5. Xin Gao & Gengxin Duan & Chunguang Lan, 2021. "Bayesian Updates for an Extreme Value Distribution Model of Bridge Traffic Load Effect Based on SHM Data," Sustainability, MDPI, vol. 13(15), pages 1-15, August.
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