Abstract
Accurately evaluating rockburst intensity has attracted much attention in these recent years, as it can guide the design of engineering in deep underground conditions and avoid injury to people. In this study, a new ensemble classifier combining a random forest classifier (RF) and beetle antennae search algorithm (BAS) has been designed and applied to improve the accuracy of rockburst classification. A large dataset was collected from across the world to achieve a comprehensive representation, in which five key influencing factors were selected as the input variables, and the rockburst intensity was selected as the output. The proposed model BAS-RF was then validated by the dataset. The results show that BAS could tune the hyperparameters of RF efficiently, and the optimum model exhibited a high performance on an independent test set of rockburst data and new engineering projects. According to the ensemble RF-BAS model, the feature importance was calculated. Furthermore, the accuracy of the proposed model on rockburst prediction was higher than the conventional machine learning models and empirical models, which means that the proposed model is efficient and accurate.
Highlights
Rock stability in deep underground conditions is seriously affected by rockburst, which still attracts a lot of attention nowadays [1,2]
Evaluating the rockburst intensity has been a significant task as it can be a guideline in this area and guide managers to design carefully [3,4]
In short-term predictions, the rockburst occurrence is based on in-situ site testes; the long-term prediction is basically according to the fundamental methods, such as strength theory and energy theory, which are similar to simulation, machine learning, and empirical knowledge methods [9]
Summary
Rock stability in deep underground conditions is seriously affected by rockburst, which still attracts a lot of attention nowadays [1,2]. Rockburst cases occur in different conditions, such as tunneling and mining [5,6,7]. Classifying and predicting the rockburst intensity plays a significant role in working safety. Estimating and predicting rockburst intensity before its occurrence is of importance. In short-term predictions, the rockburst occurrence is based on in-situ site testes; the long-term prediction is basically according to the fundamental methods, such as strength theory and energy theory, which are similar to simulation, machine learning, and empirical knowledge methods [9]. There are various machine learning methods for predicting long-term rockburst hazards, such as support vector machines, artificial neural networks, and decision trees. It can be noted that the prediction accuracy of rockburst intensity is affected by the number of data and different machine learning algorithms. Developing a high-performance and less-time-consuming ensemble classifier for the larger dataset is quite important
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