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Artificial intelligence-assisted method of structural health monitoring of subway shield tunnel based on insulation degradation location

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  • Yiwei Zhao
  • Chengtao Wang
  • Yi Tao

Abstract

Stray current leakages inevitably arise to seriously threaten the reliability of the subway tunnel structure and third-party system through electrochemical corrosion. Location of stray current leakage provides novel approach to mitigate stray current corrosion and valid guidance for maintenance of subway tunnel. The present study is dedicated to develop an integrated network-based method for locating the insulation degradation in subway tunnel. In this work, a predictive model based on LWQPSO-SOM algorithm is designed to cluster the risk level of stray current leakage through data mining. Then, an evaluation index Phigh is proposed to calculate the probability of high-risk index in the clustering results. Identification results of leakage zone of stray current is effectively validated through the measurement of rail-to-earth conductance. Results showed that the distribution of Phigh is highly related with rail-to-earth conductance distribution, indicating that the proposed method is applicable for stray current leakage location in the subway tunnel and potentially applicable in engineering fields.

Suggested Citation

  • Yiwei Zhao & Chengtao Wang & Yi Tao, 2025. "Artificial intelligence-assisted method of structural health monitoring of subway shield tunnel based on insulation degradation location," PLOS ONE, Public Library of Science, vol. 20(6), pages 1-22, June.
    • Handle: RePEc:plo:pone00:0325296
    DOI: 10.1371/journal.pone.0325296
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    References listed on IDEAS

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    1. Huang, Yunfei & Qin, Guojin & Yang, Ming & Nogal, Maria, 2025. "Dynamic quantitative assessment of service resilience for long-distance energy pipelines under corrosion," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
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