滑坡概率在离子型稀土矿边坡稳定性分析中的应用研究

发布时间：2018-06-27 10:15

本文选题：滑坡概率 + 逻辑回归模型　；参考：《江西理工大学》2017年硕士论文

【摘要】：本文以赣南离子型稀土矿区边坡为例,研究滑坡概率在边坡稳定性分析中的应用。论文的主要研究工作及结论如下:(1)滑坡概率模型及应用。研究根据赣南离子型稀土矿区边坡的实际概况,选定滑坡关键影响因素,通过边坡现场调查、岩土参数测定、数据监测等3种方式获取了21个边坡样本,采用逻辑回归模型结合确定性系数CF建立滑坡概率模型,由滑坡概率模型和边坡样本进行实例应用。选用龙南县东江乡足洞试验矿某边坡作为工程实例,进行滑坡概率求解,包括确定子集区间分类及各子集区间确定性系数CF、边坡样本各参数转化为确定性系数CF值、应用SPSS软件进行逻辑回归等,得出滑坡概率求解方程,继而对目标边坡滑坡概率进行预测,结果表明滑坡概率适用于边坡稳定性分析。(2)滑坡概率模型优化。滑坡概率分析边坡稳定性其精度和准确性存在瑕疵,因此对其进行了3种优化,包括滑坡影响因素子集区间选取、边坡状态取值以及降低滑坡影响因素间的相关性。研究选取了两种不同的子集区间分类方法对子集区间选取进行优化,结果表明采用I类方法即步长分类法较为适用。比较不同边坡状态取值进行拟合分析,研究结果表明模型的准确性不受取值影响,但预测结果存在的偏差和精度受取值影响,取值越大,精度越高,但偏差存在的可能性越大,实际应用时,应根据实际需求和边坡样本精度选取边坡状态取值。降低滑坡影响因素间的相关性可以降低滑坡因子间相关性,可以对模型进行优化,研究采用安全系数法,假定单一影响因素为特定变量进行滑坡影响因素优化,结果表明对原回归项中显著性不明显的回归项进行优化,其结果得到明显的优化;而原回归项中显著性非常明显的回归项,对其进行优化,则优化结果不明显;模型整体优化效果明显。(3)滑坡概率与安全系数应用比较和关系分析。分别求解样本边坡的滑坡概率和安全系数,并进行等级划分,结果表明滑坡概率和安全系数评价边坡稳定性都有较好的适用性和准确性,且其工程等级划分接近一致。同时对滑坡概率和安全系数进行函数关系推导,并进行拟合和验证,可以推导出二者存在函数关系,其结果表示为:lnP/(1-P)=f(F_s)=20.2-16.0F_s。
[Abstract]:In this paper, the application of landslide probability in slope stability analysis is studied by taking the slope of ion type rare earth mining area in Gannan as an example. The main research work and conclusions are as follows: (1) landslide probability model and its application. According to the actual situation of the slope of ion type rare earth mining area in south Jiangxi province, the key influencing factors of landslide were selected, and 21 slope samples were obtained through slope field investigation, geotechnical parameter measurement and data monitoring. The probability model of landslide is established by means of logical regression model combined with deterministic coefficient CF. The landslide probability model and slope sample are used for practical application. A slope in Dongjiang Township Test Mine of Longnan County was selected as an engineering example to solve the landslide probability, including determining the classification of subsets and deterministic coefficient CFCs of each subset, and converting each parameter of slope sample into deterministic coefficient CF value. Using SPSS software to solve the equation of landslide probability, the landslide probability is predicted. The results show that the landslide probability is suitable for slope stability analysis. (2) landslide probability model optimization. The probability analysis of slope stability has defects in accuracy and accuracy. Therefore, three kinds of optimization are carried out, including the selection of subsets of landslide influencing factors, the selection of slope state values and the reduction of the correlation among landslide influencing factors. Two different subset interval classification methods are selected to optimize the subset interval selection. The results show that the class I method, i.e. step size classification, is more suitable. The results show that the accuracy of the model is not affected by the values, but the deviation and accuracy of the prediction results are affected by the values. The greater the value, the higher the accuracy, but the greater the possibility of deviation. In practical application, the value of slope state should be selected according to actual demand and slope sample precision. Reducing the correlation between landslide factors can reduce the correlation between landslide factors, and the model can be optimized. The safety factor method is adopted to optimize the landslide influencing factors, assuming that a single influencing factor is a specific variable. The results show that the results of optimization of the regression terms which are not significant in the original regression term are obviously optimized, but the results are not obvious if the regression terms are very significant in the original regression term. The overall optimization effect of the model is obvious. (3) the application of landslide probability and safety factor is compared and the relationship between landslide probability and safety factor is analyzed. The landslide probability and safety factor of the sample slope are calculated and classified respectively. The results show that the landslide probability and safety factor have good applicability and accuracy in evaluating the slope stability, and the engineering grade classification is close to the same. At the same time, the function relation between landslide probability and safety factor is deduced, and the functional relationship between them can be deduced by fitting and verifying. The result is expressed as 20. 2-16. 0 FSD / (1-P) f (FSP) and 20. 2-16. 0 FSP.
【学位授予单位】：江西理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TD854.6;TD865

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