Factor of safety prediction for slope stability using PCA and BPNN in Guangdong’s H mining area

Evaluating slope failure is a primary concern in geotechnical engineering, and employing advanced machine learning techniques to design Factor of Safety (FOS) has become a critical focus. This study introduces a method that integrates Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) to predict the FOS. Compared to existing machine learning design approaches, the PCA-BPNN method demonstrates superior accuracy, achieving an R2 of 0.917, RMSE of 0.061, and MAE of 0.047 for the training set, and an R2 of 0.879, RMSE of 0.071, and MAE of 0.057 for the testing set. This method is applied to assess the slope stability of the H mining area in Guangdong, China, resulting in a designed FOS of 1.409, which meets practical engineering requirements. The findings highlight the effectiveness of the PCA-BPNN method in enhancing slope stability assessments in geotechnical applications.