Comprehensive Analysis of Multiple Machine Learning Techniques for Rock Slope Failure Prediction

In this study, twelve machine learning (ML) techniques are used to accurately estimate the safety factor of rock slopes (SFRS). The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran, evenly distributed between the training (80%) and testing (20%) datasets. The models are evaluated for accuracy using Janbu's limit equilibrium method (LEM) and commercial tool GeoStudio methods. Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS (MSE = 0.0182, R2 = 0.8319) and shows high agreement with the results from the LEM method. The results from the long-short-term memory (LSTM) model are the least accurate (MSE = 0.037, R2 = 0.6618) of all the models tested. However, only the null space support vector regression (NuSVR) model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant. It is suggested that this model would be the best one to use to calculate the SFRS. A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties. In this study, we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.