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Study on the Reliability Evaluation Method and Diagnosis of Bridges in Cold Regions Based on the Theory of MCS and Bayesian Networks

Author

Listed:
  • Zhonglong Li

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

  • Wei Ji

    (CCCC Infrastructure Maintenance Group Co., Ltd., Beijing 100020, China)

  • Yao Zhang

    (School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
    Henan Provincial Communications Planning & Design Institute Co., Ltd., Zhengzhou 450018, China)

  • Sijia Ge

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

  • Haonan Bing

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

  • Mingjun Zhang

    (Heilongjiang Communications Investment Group Co., Ltd., Harbin 150001, China)

  • Zhifeng Ye

    (Heilongjiang Highway Development Center, Harbin 150001, China)

  • Baowei Lv

    (Heilongjiang Highway Development Center, Harbin 150001, China)

Abstract

The safety assessment of bridges in cold areas under the special environmental effects of extremely low temperatures, frequent freezing and thawing, and chloride ion erosion from snow removal with deicing salt, presents challenges that requiring solving. Thus, this paper proposes a new method of safety assessment based on a combination of Monte Carlo simulation (MCS) and Bayesian theory that achieves the reliability evaluation and reverse diagnosis of the overall safety performance of reinforced concrete bridges in cold areas. Additionally, the new method accomplishes the intelligent grading of various safety performance aspects of the bridge, which provides substantial references for the maintenance and reinforcement of in-service bridges.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13786-:d:951819
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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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