Cloud model for stability evaluation of recently failed soil slopes based on weight inversion of influencing factors
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摘要:
在强降雨等因素影响下，新近失稳土质边坡易再次发生滑动，并对现场救援人员的安全构成威胁。如何对该类边坡的稳定性进行快速、准确地评价，亟须解决。由于古滑坡的滑动面处土体的抗剪强度随着时间的推移有所提高，故无法直接套用古滑坡复活的评价方法。常用的极限平衡条分法或有限元等数值分析法又需要事先进行现场勘察，耗时较长影响救援进度。因云模型评价方法对评价因子的精度要求较低，可弥补上述方法的不足之处。但目前对云模型评价因子权重的研究，仍存在一些不足之处，故提出采用反分析法来计算各评价因子的权重。选取坡高等9个易于获取且是决定边坡稳定性的主要因素为评价因子，参照《地质灾害调查技术要求》和前人的研究成果对各评价因子的稳定分级区间进行划分，利用MATLAB程序语言平台生成相应的综合云模型。根据滑坡前各评价因子的数值反分析其权重的云模型特征参数，建立可方便快捷地对新近失稳边坡进行稳定性评价的云模型，使用Python 语言和Qt Designer工具进行应用程序的开发。使用该应用程序对福建省永春县冷水村一新近失稳边坡2016年11月3—8日的稳定性进行评价，得到的结果与现场情况基本吻合，初步验证了该方法准确性。该程序的运行过程耗时较短，也验证了该方法的快速性。
Abstract:The stability of newly failed soil slopes, particularly under the influence of heavy rainfall, presents a significant threat to the safety of on-site rescue personnel. It is urgent to find a quick and accurate method for evaluating the stability of such slopes. The evaluation methods used for reactivating ancient landslides cannot be directly applied because the shear strength of the soil at the sliding surface of ancient landslides improves over time. Common numerical analysis methods such as limit equilibrium slice method or finite element method require time-consuming on-site surveys, which may affect the progress of rescue operations. The cloud model evaluation method is suitable for evaluating the stability of these slopes as it has lower accuracy requirements for evaluation factors, thus compensating for the limitations of the aforementioned methods. However, the current research on the weight of evaluation factors in cloud model evaluation still has some deficiencies. Therefore, a weight inversion method is proposed to calculate the weights of each evaluation factor. Nine primary influencing factors, including slope height, which are easily obtainable, were selected as evaluation factors. By referencing the "Technical Requirements for Geological Hazard Investigation" and previous research findings, grading intervals for each evaluation factor were determined. The corresponding comprehensive cloud model was generated using the MATLAB programming platform. According to the value of each evaluation factor before sliding, the cloud model’s characteristic parameters for weight inversion were obtained. And the cloud model for evaluating the stability of the newly failed slope was established. The Python language and the Qt Designer tool were used to develop the application of stability evaluation. The stability of a newly failed slope in Lengshui Village, Yongchun County, Fujian Province, during November 3-8 2016, was assessed using this application. The result align closely with the actual on-site conditions, validating the accuracy of the proposed evaluation method. The efficient runtime of the application further demonstrates its speed.
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Key words:
- inversion /
- newly failed slope /
- cloud model /
- stability evaluation /
- application
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表 1 边坡稳定性评价因子各等级区间
Table 1. Grade intervals of slope stability evaluation factors
评价
因子分级区间 不稳定 欠稳定 基本稳定 稳定 H/m [50, 70) [30, 50) [10, 30) [0, 10) β/（°） [50, 90) [35, 50) [20, 35) [0, 20) c/kPa [0, 23.5) [23.5, 47） [47, 70.5) [70.5, 94） φ/（°） [15, 21.75) [21.75, 28.5) [28.5, 32.25) [32.25, 42） γsat/
（kN·m−3）[23.5, 21.5) [21.5, 19.5) [19.5, 17.5) [17.5, 15.5) P/mm [50, 100) [25, 50) [10, 25) [0, 10) T/h [18, 24） [12, 18） [6, 12） [0, 6） I/
（mm·d−1）[50, 100) [25, 50) [10, 25) [0, 10) η [0, 0.25) [0.25, 0.5) [0.5, 0.75) [0.75, 1] 
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表 2 各评价因子的权重云模型特征参数
Table 2. Characteristic parameters of weight cloud model for each evaluation factors
评价因子 期望 熵 评价因子 期望 熵 H/m 0.160 0.010 P/mm 0.134 0.027 β/（°） 0.080 0.000 T/h 0.134 0.046 c/kPa 0.103 0.027 I/（mm·d−1） 0.131 0.046 φ/（°） 0.103 0.027 η 0.040 0.000 γsat/（kN·m-3） 0.115 0.027
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表 3 2016年11月3—8日的降雨情况
Table 3. Rainfall distribution from November 3 to 8, 2016
日期 降雨量/mm 降雨历时/h 降雨强度/（mm·d−1） 前期降雨量/mm 11月3日 0 0 0 10.5 11月4日 29.6 12 59.2 8.4 11月5日 62.5 24 62.5 30.4 11月6日 20.8 24 20.8 74.3 11月7日 13.2 12 26.4 76.1 11月8日 0 0 0 71.4
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表 4 2016年11月3—8日的边坡稳定性评价结果
Table 4. Stability evaluation results of landslide body from November 3 to 8, 2016
日期 不稳定 欠稳定 基本稳定 稳定 评价结果 隶属度区间 平均
隶属度隶属度区间 平均
隶属度隶属度区间 平均
隶属度隶属度区间 平均
隶属度11月3日 [0.036,0.112] 0.074 [0.113,0.124] 0.119 [0.043,0.103] 0.073 [0.168,0.661] 0.415 稳定 11月4日 [0.049,0208] 0.129 [0.132,0.247] 0.190 [0.049,0.144] 0.097 [0.126,0.401] 0.264 稳定 11月5日 [0.083,0.392] 0.238 [0.132,0.261] 0.197 [0.044,0.087] 0.066 [0.099,0.26] 0.180 不稳定 11月6日 [0.093,0.427] 0.260 [0.11,0.135] 0.123 [0.059,0.19] 0.125 [0.092,0.247] 0.170 不稳定 11月7日 [0.068,0.288] 0.178 [0.13,0.248] 0.189 [0.056,0.2] 0.128 [0.099,0.265] 0.182 欠稳定 11月8日 [0.062,0.263] 0.163 [0.109,0.127] 0.118 [0.035,0.049] 0.042 [0.149,0.562] 0.356 稳定
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