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Steady-State Data Baseline Model for Nonstationary Monitoring Data of Urban Girder Bridges

Author

Listed:
  • Shaoyi Zhang

    (School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, China
    Department of Astronautic Science and Mechanics, Harbin Institute of Technology, No. 92 West Dazhi Street, Harbin 150001, China)

  • Yongliang Wang

    (Jinan Huanghe Luqiao Construction Group Co., Ltd., 5111 Aoti Middle Road, Jinan 250000, China)

  • Kaiping Yu

    (Department of Astronautic Science and Mechanics, Harbin Institute of Technology, No. 92 West Dazhi Street, Harbin 150001, China)

Abstract

In bridge structural health monitoring systems, an accurate baseline model is particularly important for identifying subsequent structural damage. Environmental and operational loads cause nonstationarity in the strain monitoring data of urban girder bridges. Such nonstationary monitoring data can mask damage and reduce the accuracy of the established baseline model. To address this problem, a steady-state data baseline model for bridges is proposed. First, for observable effects such as ambient temperature, a directional projection decoupling method for strain monitoring data is proposed, which can reduce the nonstationary effect of ambient temperature, and the effectiveness of this method is proven using equations. Second, for unobservable effects such as traffic load, a k-means clustering method for steady state of traffic loads is proposed; using this method, which can divide the steady and nonsteady states of traffic loads and reduce the nonstationary effect of traffic loads on strain monitoring data, a steady-state baseline model is established. Finally, the effectiveness of the steady-state baseline model is verified using an actual bridge. The results show that the proposed baseline model can reduce the error caused by nonstationary effects, improve the modelling accuracy, and provide useful information for subsequent damage identification.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12134-:d:924808
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    References listed on IDEAS

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    1. Jiawang Zhan & Chuang Wang & Zhiheng Fang, 2021. "Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    2. Xiaoming Lei & Limin Sun & Ye Xia & Tiantao He, 2020. "Vibration-Based Seismic Damage States Evaluation for Regional Concrete Beam Bridges Using Random Forest Method," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
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    Cited by:

    1. 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.

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