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Causality-based multi-model ensemble learning for safety assessment in metro tunnel construction

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  • Chang, Leilei
  • Zhang, Limao
  • Xu, Xiaobin

Abstract

The safety of the nearby buildings to the metro lines is directly affected by the underground metro tunnel construction (MTC) activities. In this study, a new causality-based multi-model ensemble learning approach is proposed for the safety assessment of MTC. First, data causality is defined to reflect the causal relation between the assessment input and output, and it is calculated using an improved ensemble learning approach. Then, multiple sub-models are constructed using different sub-datasets which are classified according to the data causality. Third, the weights of sub-multiple models are calculated according to the respective accuracy of the sub-models and the matching degrees between the new input and different sub-datasets. Finally, a unified output is obtained by integrating the outputs from sub-models while considering their respective weights. A practical case of building tilt rate (BTR) assessment of Metro Line 6 in the city of Wuhan, China, is studied. Case study results show that the proposed approach outperforms (1) using a single sub-model and several other machine learning approaches, and also (2) not adopting the data causality to classify sub-datasets. Moreover, how varied settings of the sub-datasets classification ratios and weight thresholds would affect the performance is also investigated.

Suggested Citation

  • Chang, Leilei & Zhang, Limao & Xu, Xiaobin, 2023. "Causality-based multi-model ensemble learning for safety assessment in metro tunnel construction," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000832
    DOI: 10.1016/j.ress.2023.109168
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    References listed on IDEAS

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    1. Xu, Zizhen & Chopra, Shauhrat S., 2022. "Network-based Assessment of Metro Infrastructure with a Spatial–temporal Resilience Cycle Framework," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. Wei, Pengfei & Zheng, Yu & Fu, Jiangfeng & Xu, Yuannan & Gao, Weikai, 2023. "An expected integrated error reduction function for accelerating Bayesian active learning of failure probability," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Nagulapati, Vijay Mohan & Lee, Hyunjun & Jung, DaWoon & Brigljevic, Boris & Choi, Yunseok & Lim, Hankwon, 2021. "Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Ouyang, Min & Liu, Chuang & Wu, Shengyu, 2020. "Worst-case vulnerability assessment and mitigation model of urban utility tunnels," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
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