Reliability Analysis of Spatially Variable Soil Slope Using Deep Learning Algorithm

Landslides are the most disastrous natural catastrophe, which causes immense damage to infrastructure and loss of life globally. The properties of soil slope are spatially variable due to various loading and deposition conditions. Hence, slope reliability analysis should take into account the spatial variation of slope material. Previous researchers utilize the random field theory to consider spatial variability in slope reliability analysis. However, this method requires extensive computational resources and time. To address this issue, the present study proposes a methodology based on convolution neural network (CNN) and Monte Carlo simulation. CNN algorithm is utilized as a surrogate model to replicate the random field model of slope as CNN algorithms efficiently learn the spatial variation of the random field. The CNN model is further used to conduct reliability analysis using Monte Carlo simulation. An example application of the proposed method is performed for spatially variable soil slope to validate the proposed method. The example results suggest that the proposed method provides practical values of the probability of slope failure.