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Digital Model of Deflection of a Cable-Stayed Bridge Driven by Deep Learning and Big Data Optimized via PCA-LGBM

Author

Listed:
  • Zili Xia

    (Hong Kong-Zhuhai-Macao Bridge Authority, Zhuhai 519060, China)

  • Junxiao Guo

    (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China)

  • Zixiang Yue

    (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China)

  • Youliang Ding

    (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China)

  • Zhiwen Wang

    (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China
    Shenzhen Express Engineering Consulting Co., Ltd., Shenzhen 518000, China)

  • Shouwang Sun

    (Shenzhen Express Engineering Consulting Co., Ltd., Shenzhen 518000, China)

Abstract

Based on big data, we can build a regression model between a temperature field and a temperature-induced deflection to provide a control group representing the service performance of bridges, which has a positive effect on the full life cycle maintenance of bridges. However, the spatial temperature information of a cable-stayed bridge is difficult to describe. To establish a regression model with high precision, the improved PCA-LGBM (principal component analysis and light gradient boosting machine) algorithm is proposed to extract the main temperature features that can reflect the spatial temperature information as accurately and efficiently as possible. Then, in this article, we searched for a suitable digital tool for modeling the regressive relationship between the temperature variables and the temperature-induced deflection of a cable-stayed bridge. The multiple linear regression model has relatively low precision. The precision of the backpropagation neural network (BPNN) model has been improved, but it is still unsatisfactory. The nested long short-term memory (NLSTM) model improves the nonlinear expression ability of the regression model and is more precise than BPNN models and the classical LSTM. The architecture of the NLSTM network is optimized for high precision and to avoid the waste of computational costs. Based on the four main temperature features extracted via the PCA-LGBM, the NLSTM network with double hidden layers and 256 hidden units in each hidden layer has much higher precision than the other regression models. For the NLSTM regression model of the temperature-induced deflection of a cable-stayed bridge, the mean absolute error is only 4.76 mm, and the mean square error is only 18.57 mm 2 . The control value of the NLSTM regression model is precise and thus provides the potential for early detection of bridge anomalies. This article can provide reference processes and a data extraction algorithm for deflection modeling of other cable-stayed bridges.

Suggested Citation

  • Zili Xia & Junxiao Guo & Zixiang Yue & Youliang Ding & Zhiwen Wang & Shouwang Sun, 2023. "Digital Model of Deflection of a Cable-Stayed Bridge Driven by Deep Learning and Big Data Optimized via PCA-LGBM," Sustainability, MDPI, vol. 15(12), pages 1-25, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9623-:d:1172007
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    References listed on IDEAS

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    1. Fang, Kuangnan & Fan, Xinyan & Zhang, Qingzhao & Ma, Shuangge, 2018. "Integrative sparse principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 1-16.
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