The random field finite element method (RF-FEM) provides a robust tool for carrying out slope reliability analysis that incorporates the spatial variability of soil properties. However, it has a major drawback of being computationally very time-consuming. To address this common criticism, the current study proposes a novel metamodel-based method for efficient slope reliability analysis in spatially variable soils. The proposed method involves the use of Convolutional Neural Networks (CNNs) as metamodels of the random field finite element model. With proper training using a small but sufficient number of random field samples, the CNN can potentially replace the computationally demanding random field finite element analyses for Monte-Carlo simulations. This paper examines the capability of CNNs to learn high-level features that contain information about the random variabilities in both spatial distribution and intensity, and the accuracy of the subsequent predictions of the RF-FEM results. Application of the proposed method to slope reliability analysis in spatially variable soils is illustrated and compared against other metamodel-based approaches, using a case study involving a multi-layered soil system with randomly varying cohesion c and the friction angle ϕ. The results show that (i) the proposed CNN approach predicts a probability of slope failure that is within 5% of the corresponding value obtained using direct RF-FEM Monte-Carlo simulations, but at a small fraction of the computational cost, and (ii) the proposed method also compares favourably against other metamodel-based methods in terms of computational efficiency and accuracy.

随机场有限元方法（RF-FEM）为进行边坡可靠度分析提供了一个强大的工具，该分析结合了土壤特性的空间变异性。但是，它的主要缺点是计算上非常耗时。为了解决这种普遍的批评，当前的研究提出了一种新颖的基于元模型的方法，用于在空间可变土壤中进行有效的边坡可靠性分析。所提出的方法涉及使用卷积神经网络（CNN）作为随机场有限元模型的元模型。通过使用少量但足够数量的随机场样本进行适当的训练，CNN可以潜在地取代对蒙特卡洛模拟的计算要求高的随机场有限元分析。本文研究了CNN学习高级特征的能力，这些特征包含有关空间分布和强度的随机变异性以及随后的RF-FEM结果预测准确性的信息。举例说明了该方法在空间可变土壤中的边坡可靠度分析中的应用，并将其与其他基于元模型的方法进行了比较，并以一个涉及具有随机变化内聚力的多层土壤系统为例c和摩擦角ϕ。结果表明（i）提出的CNN方法预测的边坡破坏概率在使用直接RF-FEM蒙特卡洛模拟获得的相应值的5％以内，但计算成本很小，并且（ii ）所提出的方法在计算效率和准确性方面也优于其他基于元模型的方法。