Rockburst Intensity Grade Prediction Model Based on Batch Gradient Descent and Multi-Scale Residual Deep Neural Network

Yu Zhang, Mingkui Zhang*, Jitao Li, Guangshu Chen

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

Rockburst is a phenomenon in which free surfaces are formed during excavation, which subsequently causes the sudden release of energy in the construction of mines and tunnels. Light rockburst only peels off rock slices without ejection, while severe rockburst causes casualties and property loss. The frequency and degree of rockburst damage increases with the excavation depth. Moreover, rockburst is the leading engineering geological hazard in the excavation process, and thus the prediction of its intensity grade is of great significance to the development of geotechnical engineering. Therefore, the prediction of rockburst intensity grade is one problem that needs to be solved urgently. By comprehensively considering the occurrence mechanism of rockburst, this paper selects the stress index (σθc), brittleness index (σct), and rock elastic energy index (Wet) as the rockburst evaluation indexes through the Spearman coefficient method. This overcomes the low accuracy problem of a single evaluation index prediction method. Following this, the BGD-MSR-DNN rockburst intensity grade prediction model based on batch gradient descent and a multi-scale residual deep neural network is proposed. The batch gradient descent (BGD) module is used to replace the gradient descent algorithm, which effectively improves the efficiency of the network and reduces the model training time. Moreover, the multi-scale residual (MSR) module solves the problem of network degradation when there are too many hidden layers of the deep neural network (DNN), thus improving the model prediction accuracy. The experimental results reveal the BGD-MSR-DNN model accuracy to reach 97.1%, outperforming other comparable models. Finally, actual projects such as Qinling Tunnel and Daxiangling Tunnel, reached an accuracy of 100%. The model can be applied in mines and tunnel engineering to realize the accurate and rapid prediction of rockburst intensity grade.

源语言英语
页(从-至)1987-2006
页数20
期刊Computer Systems Science and Engineering
47
2
DOI
出版状态已出版 - 2023
已对外发布

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Zhang, Y., Zhang, M., Li, J., & Chen, G. (2023). Rockburst Intensity Grade Prediction Model Based on Batch Gradient Descent and Multi-Scale Residual Deep Neural Network. Computer Systems Science and Engineering, 47(2), 1987-2006. https://doi.org/10.32604/csse.2023.040381