Image-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning

Abstract

Landslides account for approximately 5% of natural disasters resulting in significant socio-economic impacts. As a major infrastructure issue, slope stability has been traditionally analyzed with multiple deterministic and probabilistic methods to evaluate the stability of slopes or the probability of landslides. Geotechnical engineers tend to visit the sites of slopes, measure the geometry and soil properties, and use those traditional methods to analyze the slope stability and provide a factor of safety evaluation and recommendation. The fast-growing new technologies such as the internet of things and big data analytics provide new directions for natural hazard prevention. This study is the first to use deep learning as a new method for slope stability analysis for landslide prevention. A convolutional neural network was used to establish the model via transfer learning for processing simulated slope images. After training, our model can accurately predict the factor of safety of slopes for new slope images. Our proposed method was validated by comparing it with a classic limit equilibrium method, i.e., the simplified Bishop method, which is widely used in commercial programs for slope stability analysis. The comparison results showed that our proposed deep learning method outperformed the traditional method by decreasing the computation time by orders of magnitude without sacrificing accuracy. The results demonstrated the possibility and advantages of using deep learning as a new type of slope stability analysis method, including its ability to analyze raw image data directly, high level of automation, satisfactory accuracy, and short computing time, which will enable onsite evaluation for slope stability analysis. Thus, it facilitates fast in-situ decision-making for geotechnical applications and ensures the feasibility of using the internet of things and big data analytics for natural hazard prevention.