Convolutional neural networks prediction of the factor of safety of random layered slopes by the strength reduction method

The strength reduction method is often used to predict the stability of soil slopes with complex soil properties and failure mechanisms. However, it requires a considerable computational effort. In this paper, we make use of a convolutional neural network to reduce the computational cost. The factor of safety of 600 slopes with different inclination and soil properties is first calculated with the strength reduction method. A convolutional neural network is then trained and validated. We demonstrate the performance of our approach and show how to augment the dataset to further enhance its capability and prevent overfitting.

Automated summary

In this study, a convolutional neural network is used to reduce the computational effort in predicting the stability of slopes with complex soil properties. The factor of safety of 600 slopes with different inclination and soil properties is calculated using the strength reduction method. The trained convolutional neural network is then validated, and the augmentation of the dataset is demonstrated to enhance its capability and prevent overfitting.