Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Soil-rock mixtures are geological materials with complex physical and mechanical properties. Therefore, the stability prediction of soil-rock mixture slopes using machine learning methods is an important topic in the field of geological engineering. This study uses the soil-rock mixture slopes investigated in detail as the dataset. An intelligent optimization algorithm-weighted mean of vectors algorithm (INFO) is coupled with a machine learning algorithm. One of the new ensemble learning models, which named IN-Voting, is coupled with INFO and voting model. Twelve single machine learning models and sixteen novel IN-Voting ensemble learning models are built to predict the stability of soil-rock mixture slopes. Then, the prediction accuracies of the above models are compared and evaluated using three evaluation metrics: coefficient of determination (R-2), mean square error (MSE), and mean absolute error (MAE). Finally, an IN-Voting ensemble learning model based on five weak learners is used as the final model for predicting the stability of soil-rock mixture slopes. This model is also used to analyze the importance of the input parameters. The results show that: 1) Among 12 single machine learning models for the stability prediction of soil-rock mixture slopes, MLP (Multilayer Perceptron) has the highest prediction accuracy. 2) The IN-Voting model has higher prediction accuracy than single machine learning models, with an accuracy of up to 0.9846) The structural factors affecting the stability of soil-rock mixture slopes in decreasing order are the rock content, bedrock inclination, slope height, and slope angle.