A probabilistic method for evaluating wedge stability based on blind data theory

作者：Ma, Zhongjun; Qin, Shengwu^*; Chen, Junjun; Lv, Jiangfeng; Chen, Jianping; Zhao, Xiaolan

来源：Bulletin of Engineering Geology and the Environment, 2019, 78(3): 1927-1936.

DOI：10.1007/s10064-017-1204-3

• Wedge
• Discontinuity
• Uncertainty
• Blind data model
• Probability analysis
• Monte-Carlo simulation

This study proposes a probabilistic approach based on blind data theory and a k-means clustering algorithm to analyze the wedge stability problem considering multiple failure modes. The method evaluates the stability of the wedge by constructing a blind data model that can comprehensively consider the various uncertainties of the wedge parameters. The construction of the blind data evaluation model of wedge stability is described, and the blind evaluation model used to determine the wedge stability with wedge failure dominated by a prone-sliding face and double-face failure is given. The model is applied to analyze the stability of a classical double-face wedge. The results are compared with the probability of failure evaluated by a Monte-Carlo simulation, as well as the results obtained from deterministic analysis, including the mean safety factor and SWEDGE numerical analysis methods. The probability distribution for the safety factor of the wedge determined by this method is fairly consistent with those from the Monte-Carlo simulation method, verifying the reliability and efficiency of the new method. Finally, the method is applied to the stability evaluation of the wedge in Huludao city, Liaoning Province, China. The probability of safety factors less than 1 is 89.8%, and the probability that the safety factor is less than 1.2 is 100%, which is consistent with the actual situation. This result demonstrates that the blind data model for evaluating wedge stability exhibits good generalization performance.

• 出版日期2019-4
• 单位吉林大学