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A cloud model-based approach for risk analysis of excavation system

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  • Shen, Shui-Long
  • Lin, Song-Shun
  • Zhou, Annan

Abstract

Excavation construction in urban dense areas is regarded as a high-risk activity since there are various aspects involved and risk usually occurs in local part of excavation. This paper presents a cloud model-based approach to evaluate risk status of excavation. Multi-source information related to excavation can be coped with the developed approach. Approach development involves three phases: multi-sources information collection, construction of the benchmark cloud model (BCM) and identified cloud model (ICM), and risk level determination. Two-stage weight determination is developed to aggregate multi-sources information. An excavation project in Zhuhai is adopted to verify the performance of developed approach. Excavation was divided into subsystems according to monitoring plan of factors and risk status of subsystems is estimated based on the similar membership degree calculation between BCM and ICM. Results were found to be consistent with field situation. The sensitivity factors towards estimated risk status results were identified through correlation analysis. This proposed approach provides a novel way of perceiving the risk status of excavation and guidelines for risk management.

Suggested Citation

  • Shen, Shui-Long & Lin, Song-Shun & Zhou, Annan, 2023. "A cloud model-based approach for risk analysis of excavation system," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022005993
    DOI: 10.1016/j.ress.2022.108984
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    References listed on IDEAS

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    1. Pan, Yue & Qin, Jianjun & Hou, Yongmao & Chen, Jin-Jian, 2024. "Two-stage support vector machine-enabled deep excavation settlement prediction considering class imbalance and multi-source uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Bo Wu & Yu Wei & Guowang Meng & Shixiang Xu & Qinshan Wang & Dianbin Cao & Chenxu Zhao, 2023. "Multi-Source Monitoring Data Fusion Comprehensive Evaluation Method for the Safety Status of Deep Foundation Pit," Sustainability, MDPI, vol. 15(15), pages 1-18, August.

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