Recently, the random field finite element method (RF-FEM) has attracted significantly increasing attention in the field of geotechnical engineering, especially for the purpose of analyzing the response of geotechnical systems due to the inherent variability of physical and mechanical properties. However, the method requires repeated finite element calculations based on a mass of sampling processes, making the computing effort expensive. The surrogate model is one of the techniques commonly adopted to alleviate the computational burden. In addition, some architectures of deep learning surrogate models are so unique that it is difficult to transfer to similar cases and to be familiar and reproducible by readers. In this study, we propose a convolutional neural network (CNN) surrogate model based on classical architecture, VGG6, to perform random field finite element analyses (RF-FEM). We pre-process the tabular data generated by the random filed method into an image-like format as input data. The VGG6 is used as a surrogate model to replace the original RF-FEM simulations for all subsequent calculations. The applications of the proposed method to assess wall deflection of braced excavation in clays with randomly varying cohesion c_u and the friction angle φ are illustrated and compared in different cases. The excellent agreement between the VGG6 outputs and the FEM predictions demonstrated the promising potential of using VGG6s as a surrogate model for reliability analysis in spatially variable soils. Moreover, the model saves a lot of computing time and computing power and fully proves the generalization performance of the model under various cases.

近年来，随机场有限元法（RF-FEM）在岩土工程领域引起了越来越多的关注，特别是用于分析岩土系统由于物理和力学性质的固有可变性而产生的响应。然而，该方法需要基于大量采样过程的重复有限元计算，使得计算工作成本高昂。代理模型是减轻计算负担的常用技术之一。此外，一些深度学习代理模型的架构非常独特，很难转移到类似的案例中，也很难被读者熟悉和重现。在这项研究中，我们提出了一种基于经典架构 VGG6 的卷积神经网络 (CNN) 代理模型，执行随机场有限元分析 (RF-FEM)。我们将随机字段方法生成的表格数据预处理为类似图像的格式作为输入数据。VGG6 被用作替代模型，以替代所有后续计算的原始 RF-FEM 模拟。所提出的方法在随机变化黏聚力粘土中评估支撑基坑墙体挠度的应用c _u和摩擦角φ在不同情况下进行了说明和比较。VGG6 输出与 FEM 预测之间的出色一致性证明了使用 VGG6 作为空间可变土壤可靠性分析的替代模型的巨大潜力。而且该模型节省了大量的计算时间和计算能力，充分证明了模型在各种情况下的泛化性能。