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Coupling cloud theory and concept hierarchy construction early warning thresholds for deformation safety of tailings dam

Author

Listed:
  • Shaohua Hu

    (Wuhan University of Technology)

  • Meixian Qu

    (Wuhan University of Technology)

  • Youcui Yuan

    (Wuhan University of Technology)

  • Zhenkai Pan

    (Wuhan University of Technology
    Chinese Academy of Sciences)

Abstract

Scientific calculating of deformation early warning thresholds is of great significance for identifying the abnormal operation state of tailings dams. Traditional early warning studies cannot take into account the fuzziness and randomness of monitoring data, and most of them focus on single-point and single-level early warning, which cannot accurately reflect the operational state and the risk level of failure of the tailings dam. In this study, an early warning model based on cloud theory (CT) and concept hierarchy construction (CHC) is proposed to determine a reliable warning of multi-point displacement of tailings dam. The CT is used to calculate the characteristic values of displacements, including expectation (Ex), entropy (En), and hyper entropy (He), which can eliminate the influence of fuzziness and randomness of the monitoring data. The multi-point characteristic values of displacements are integrated via the CHC method to obtain the characteristic values representing the integral qualitative concept of the tailings dam, which can overcome the limitation of single-point early warning. The normal operation value of displacement of tailings dam is calculated according to the “3En” principle, and the comprehensive early warning displacement thresholds are obtained. The displacement monitoring data of Yangjiawan tailings dam demonstrates the rationality and accuracy of the developed CT–CHC model. Our work provides a new avenue to warn of the potential failure of tailings dams. Graphical abstract

Suggested Citation

  • Shaohua Hu & Meixian Qu & Youcui Yuan & Zhenkai Pan, 2024. "Coupling cloud theory and concept hierarchy construction early warning thresholds for deformation safety of tailings dam," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(9), pages 8827-8849, July.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:9:d:10.1007_s11069-024-06553-w
    DOI: 10.1007/s11069-024-06553-w
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    References listed on IDEAS

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    1. Huizhi Duan & Yongsheng Li & Hongbo Jiang & Qiang Li & Wenliang Jiang & Yunfeng Tian & Jingfa Zhang, 2023. "Retrospective monitoring of slope failure event of tailings dam using InSAR time-series observations," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(3), pages 2375-2391, July.
    2. Chongshi Gu & Xiao Fu & Chenfei Shao & Zhongwen Shi & Huaizhi Su, 2020. "Application of Spatiotemporal Hybrid Model of Deformation in Safety Monitoring of High Arch Dams: A Case Study," IJERPH, MDPI, vol. 17(1), pages 1-25, January.
    3. Zhenxiang Jiang & Jinping He, 2016. "Method of Fusion Diagnosis for Dam Service Status Based on Joint Distribution Function of Multiple Points," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-10, August.
    4. Rose, Rodrigo L. & Mugi, Sohan R. & Saleh, Joseph Homer, 2023. "Accident investigation and lessons not learned: AcciMap analysis of successive tailings dam collapses in Brazil," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    5. Yantao Zhu & Chongshi Gu & Erfeng Zhao & Jintao Song & Zhiyun Guo, 2016. "Structural Safety Monitoring of High Arch Dam Using Improved ABC-BP Model," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, October.
    6. Lei Dong & Peng Wang & Fang Yan, 2019. "Damage forecasting based on multi-factor fuzzy time series and cloud model," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 521-538, February.
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