Abstract
Rockburst is a mine dynamic disaster caused by the rapid release of elastic strain energy of surrounding rock. As the depth of engineering project operations increases, accurate classification of rockburst intensity cannot be achieved based on conventional criteria due to high uncertainty and unpredictability of rockburst. In this regard, an AOA-Voting-Soft ensemble machine learning was proposed in this study by combining seven individual classifiers, i.e., eXtreme gradient boosting, support vector machines, multilayer perceptron, k-nearest neighbor, random forest, naive Bayesian, and gradient boosting decision Tree. In addition, outliers were eliminated by means of density-based spatial clustering of applications with noise, and CURE-MeanradiusSMOTE was adopted to obtain a balanced data structure. Furthermore, the optimal combination of classifiers in Voting was determined by the game theory and the exhaustive search method. Weights of individual learners in Voting were determined through the arithmetic optimization algorithm and fivefold cross-validation. The results show that the prediction accuracy of the ensemble algorithm proposed in this study is 4.4% higher than that of the individual classifier with optimal performance. The importance analysis indicates that the elastic energy index is the most important variable that affects rockburst intensity grades. Moreover, this rockburst ensemble method can be applied further to solve other classification problems in underground engineering projects.
Highlights
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1.
This study improves the data preprocessing method, outliers were eliminated by means of density-based spatial clustering of applications with noise, and CURE-MeanradiusSMOTE was proposed to obtain a balanced data structure.
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2.
This study presents a hybrid ensemble model for Rockburst intensity grade prediction, combining a new metaheuristic method with the Voting-Soft model.
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3.
This study combines game theory and method of exhaustion to determine the best classifier combination in voting.
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The weights of individual learners in Voting were determined through arithmetic optimization algorithm and fivefold cross-validation.
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5.
Sensitivity study was conducted on input variables with RBD-FAST, and the results suggest that \(W_{{{\text{et}}}}\) is the most important input variable.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zhi-Chao Jia, Yi Wang, Jun-Hui Wang, Qiu-Yan Pei, and Yan-Qi Zhang. The first draft of the manuscript was written by Zhi-Chao Jia and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Jia, ZC., Wang, Y., Wang, JH. et al. Rockburst Intensity Grade Prediction Based on Data Preprocessing Techniques and Multi-model Ensemble Learning Algorithms. Rock Mech Rock Eng (2024). https://doi.org/10.1007/s00603-024-03811-y
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DOI: https://doi.org/10.1007/s00603-024-03811-y