Open Access

ARTICLE

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

Yu Zhang^1,2,3, Mingkui Zhang^1,2,*, Jitao Li^1,2, Guangshu Chen^1,2

1 School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China
2 Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China
3 State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining & Technology, Beijing, 100083, China

* Corresponding Author: Mingkui Zhang. Email:

Computer Systems Science and Engineering 2023, 47(2), 1987-2006. https://doi.org/10.32604/csse.2023.040381

Received 16 March 2023; Accepted 20 April 2023; Issue published 28 July 2023

Abstract

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 (), brittleness index (), and rock elastic energy index () 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.

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