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.