Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review

Haoding Xu, Xuzhen He, Feng Shan, Gang Niu, Daichao Sheng
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Abstract

In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results in higher costs, and thus, a stochastic analysis considering uncertainty and spatial variability was developed to reduce costs. In the past few decades, machine learning has been greatly developed and extensively used in stochastic slope stability analysis, particularly used as surrogate models to improve computational efficiency. To better summarize the current application of machine learning and future research, this paper reviews 159 studies of supervised learning published in the past 20 years. The achievements of machine learning methods are summarized from two aspects—safety factor prediction and slope stability classification. Four potential research challenges and suggestions are also given.
机器学习在边坡稳定性随机分析中的研究进展
在传统的边坡稳定性分析中,假设某些“平均”或适当的“保守”性质在整个感兴趣的区域上起作用。这种确定性的保守分析往往会导致较高的成本,因此,考虑不确定性和空间变异性的随机分析可以降低成本。在过去的几十年里,机器学习在随机边坡稳定性分析中得到了很大的发展和广泛的应用,特别是作为替代模型来提高计算效率。为了更好地总结机器学习的当前应用和未来研究,本文回顾了过去20年发表的159项监督学习研究。从安全系数预测和边坡稳定性分类两个方面总结了机器学习方法的研究成果。提出了四个潜在的研究挑战和建议。
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