Slope Stability Prediction Using Principal Component Analysis and Hybrid Machine Learning Approaches

Abstract

Traditional slope stability analysis methods are time-consuming, complex, and cannot provide fast stability estimates when facing a large amount of slope cases. In this case, artificial neural networks (ANN) provide a better alternative. Based on the ANN, the particle swarm optimization (PSO) algorithm, and the principal component analysis (PCA) method, a novel PCA-PANN model is proposed. Then, a dataset of 307 slope cases covering a wide range of slope geometries and mechanical properties of geomaterial is developed. The hybrid machine learning model trained with the dataset is applied to the factor of safety (FoS) prediction of the actual slope, and three evaluation indicators are introduced to measure the prediction performance of the model. Finally, the sensitivity analysis of input parameters is carried out, and the slope protection strategy for different sensitive factors is proposed. The results show that this new model can quickly obtain the FoS and stable state of the slope without complex calculation, only by providing the relevant characteristic parameters. The correlation coefficient of the PCA-PANN model for slope stability analysis reaches more than 0.97. The sensitivity degree of influencing factors from large to small is slope angle, cohesion, pore pressure ratio, slope height, unit weight, and friction angle.