Novel approach to efficient slope reliability analysis in spatially variable soils

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 multilayered soil system with randomly varying cohesion c and the friction angle phi. 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.

Automated summary

The study introduces a metamodel-based method using Convolutional Neural Networks (CNNs) for efficient slope reliability analysis in spatially variable soils. By training CNNs with random field samples, the method can replace computationally demanding RF-FEM analyses for accurate predictions at a fraction of the cost. Results demonstrate the effectiveness of the proposed CNN approach in terms of computational efficiency and accuracy compared to other metamodel-based methods.