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Vibration-Based Seismic Damage States Evaluation for Regional Concrete Beam Bridges Using Random Forest Method

Author

Listed:
  • Xiaoming Lei

    (Department of Bridge Engineering, Tongji University, Shanghai 200092, China)

  • Limin Sun

    (State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China)

  • Ye Xia

    (Department of Bridge Engineering, Tongji University, Shanghai 200092, China)

  • Tiantao He

    (Ningbo Municipal Facilities Center, Ningbo 315010, China)

Abstract

Transportation networks play an important role in urban areas, and bridges are the most vulnerable structures to earthquakes. The seismic damage evaluation of bridges provides an effective tool to assess the potential damage, and guides the post-earthquake recovery operations. With the help of structural health monitoring (SHM) techniques, the structural condition could be accurately evaluated through continuous monitoring of structural responses, and evaluating vibration-based features, which could reflect the deterioration of materials and boundary conditions, and are extensively used to reflect the structural conditions. This study proposes a vibration-based seismic damage state evaluation method for regional bridges. The proposed method contains the measured structural dynamic parameters and bridge configuration parameters. In addition, several intensity measures are also included in the model, to represent the different characteristics and the regional diversity of ground motions. The prediction models are trained with a random forest algorithm, and their confusion matrices and receiver operation curves reveal a good prediction performance, with over 90% accuracy. The significant parameter identification of bridge systems and components reveals the critical parameters for seismic design, disaster prevention and structure retrofit.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:5106-:d:375137
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    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.

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