Deep Learning for Geotechnical Reliability Analysis with Multiple Uncertainties

Abstract

Apart from spatial variability of soil properties, a geotechnical system can have many other sources of uncertainties. To efficiently analyze such a system in a probabilistic manner, many strategies have been proposed in the literature. This paper presents a deep learning technique for an efficient geotechnical reliability analysis with multiple uncertainties. The proposed method involves using convolutional neural networks (CNNs) as metamodels of the physics-based simulation model of a geotechnical system. In the present study, the spatially variable soil properties and the external loads are simultaneously considered in the analysis of a geotechnical system. The proposed neural network method configures these uncertainties to form a multi-channel “image.” CNNs can then simultaneously learn high-level features that contain information about the multiple uncertainties. With an appropriate architecture and adequate training, the trained CNNs can replace the computationally demanding physics-based simulation model for Monte Carlo simulations. Application of the neural network method is illustrated using a synthetic geotechnical example. The results reveal that the proposed neural network method effectively handles multiple uncertainties and efficiently predicts a failure probability value that is in good agreement with the benchmark result obtained using direct Monte Carlo simulations.