Abstract This paper presents machine learning aided stochastic reliability analysis of spatially variable slopes, which significantly reduces the computational efforts and gives a complete statistical description of the factor of safety with promising accuracy compared with traditional methods. Within this framework, a small number of traditional random finite-element simulations are conducted. The samples of the random fields and the calculated factor of safety are, respectively, treated as training input and output data, and are fed into machine learning algorithms to find mathematical models to replace finite-element simulations. Two powerful machine learning algorithms used are the neural networks and the support-vector regression with their associated learning strategies. Several slopes are examined including stratified slopes with 3 or 4 layers described by 4 or 6 random fields. It is found that with 200 to 300 finite-element simulations (finished in about 5 ~ 8 h), the machine-learning generated model can predict the factor of safety accurately, and a stochastic analysis of 105 samples takes several minutes. However, the same traditional analysis would require hundreds of days of computation.

中文翻译：

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机器学习辅助空间可变坡度的随机可靠性分析

摘要 本文提出了机器学习辅助的空间变坡随机可靠性分析，与传统方法相比，它显着减少了计算工作量，并以有希望的准确性对安全系数进行了完整的统计描述。在这个框架内，进行了少量传统的随机有限元模拟。随机场的样本和计算出的安全系数分别作为训练输入和输出数据，输入机器学习算法寻找数学模型来代替有限元模拟。使用的两种强大的机器学习算法是神经网络和支持向量回归及其相关的学习策略。检查了几个斜坡，包括由 4 或 6 个随机场描述的 3 或 4 层的分层斜坡。发现通过200~300次有限元模拟（大约5~8h完成），机器学习生成的模型可以准确预测安全系数，105个样本的随机分析需要几分钟时间。然而，同样的传统分析需要数百天的计算。