The landslide of dump is a man-made geological disaster which will bring great harm to the surrounding people and environment, and probabilistic reliability analysis is commonly used to analyze the probability of slope landslide or whether protective measures should be taken. Monte Carlo simulation is the most commonly used method, but there are some problems, such as low efficiency, statistical ambiguity of small samples, and the fuzzy transition interval of the stability criterion. This paper proposes an improved Monte Carlo method that uses an improved bootstrap method to process small samples of geotechnical data, employs ELM (extreme learning machine) based on PSO (particle swarm optimization) to fit the limit equilibrium method function, and constructs the safety factor membership function of the dump site considering the fuzzy transition interval. This method was applied to an example slope of the dump site in Chengmenshan, Jiangxi. Comparing the analysis result with the result of the traditional MCS (Monte Carlo Search) method, it was found that after adding the safety factor membership function, the result was closer to the actual situation of the dump site, and the probability of failure and reliability index values were closer to those of the dangerous state; after the original function was replaced by the PSO-ELM model, the efficiency of the MCS method was greatly improved while the results maintained high consistency with the original results; the MCS method combined with the bootstrap method not only simulated the fuzzy uncertainty of the original sample statistics and distribution type but also expressed the reliability index and probability of failure as a two-sided confidence interval with a certain confidence level. The above conclusion proves the effectiveness and superiority of this method compared with the original MCS method.

中文翻译：

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基于神经网络和模糊性分析的改进蒙特卡罗方法-以城门山铜矿南坡堆场为例

垃圾场的滑坡是人为地质灾害，对周围的人和环境造成极大的危害，概率可靠性分析通常用于分析边坡滑坡的可能性或是否应采取保护措施。蒙特卡洛模拟是最常用的方法，但是存在一些问题，例如效率低，小样本的统计模糊性以及稳定性准则的模糊过渡区间。本文提出了一种改进的蒙特卡洛方法，该方法使用改进的引导程序方法来处理少量岩土数据样本，并采用基于PSO（粒子群优化）的ELM（极限学习机）来拟合极限平衡法函数，考虑模糊过渡区间，构建了垃圾场安全系数隶属函数。该方法被应用于江西城门山的一个垃圾场实例坡度。将分析结果与传统的MCS（Monte Carlo Search）方法进行比较，发现添加安全系数隶属函数后，结果更接近于垃圾场的实际情况，并且发生故障的可能性和可靠性更高指数值接近危险状态；用PSO-ELM模型代替原始函数后，MCS方法的效率大大提高，同时结果与原始结果保持高度一致性；MCS法与自举法相结合，不仅模拟了原始样本统计数据和分布类型的模糊不确定性，而且还以具有一定置信度的双向置信区间表示了可靠性指标和失败概率。以上结论证明了该方法与原始MCS方法相比的有效性和优越性。