Prediction for Rock Compressive Strength Based on Ensemble Learning and Bayesian Optimization

Rock compressive strength is an important mechanical parameter to evaluate the stability of rock mass engineering. The traditional statistical regression method has some limitations on the prediction of rock compressive strength. To this end, in this paper it proposes a method for intelligent prediction of rock compressive strength using simple rock mechanics parameters. Firstly, 620 sets of triaxial test data containing different types of rocks were collected and preprocessed. Then, three main stream ensemble learning algorithms, Random forest, XGBoost and LightGBM, were used to establish a rock compressive strength prediction model, and Bayesian optimization algorithm was used to optimize the hyperparameters during model training. Finally, the coefficient of determination (R²), mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate and compare the generalization ability of the optimized model. In addition, the importance of input features was analyzed by LGB model, to evaluate the importance of input features on the generalization ability of the model. The results show that the three models have achieved good prediction results for rock compressive strength. And the generalization ability of the LGB model is slightly better than that of the other two models (R² = 0.978, RMSE=5.58, MAPE=9.70%), and the running time is relatively minimum. Elastic modulus (E), confining pressure (σ₃) and density (ρ) have great influence on generalization ability of model, while Poisson's ratio(v)has little influence. The prediction model has good applicability to rock strength prediction, and provides a new idea for the combination of machine learning and geotechnical engineering.