Prediction of residual elastic energy index for rockburst proneness evaluation based on cluster forest model

Abstract

The residual elastic energy index is a scientific evaluation index for rockburst proneness. In laboratory test, it is sometimes difficult to obtain the post-peak curve or to test the rock sample several times, which makes it impossible to calculate the residual elastic energy index accurately. Based on 241 sets of experimental data and four input indexes of density, elastic modulus, peak intensity and peak input strain energy, this study proposed a machine learning model combining k-means clustering algorithm and random forest regression model: cluster forest (CF) model. The research employed a stratified sampling method on the dataset to ensure the representativeness and balance of the samples. Subsequently, grid search and five-fold cross-validation were utilized to optimize the model’s hyperparameters, aiming to enhance its generalization capability and prediction accuracy. Finally, the performance of the optimal model was evaluated using a test set and compared with five other commonly used models. The results indicate that the CF model outperformed the other models on the testing set, with a mean absolute error of 6.6%, and an accuracy of 93.9%. The results of sensitivity analyses reveal the degree of influence of each variable on rockburst proneness and the applicability of the CF model when the input parameters are missing. The robustness and generalization ability of the model were verified by introducing experimental data from other studies, and the results confirmed the reliability and applicability of the model. Therefore, the model not only effectively simplifies the acquisition of the residual elastic energy index, but also shows excellent performance and wide applicability.

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