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

作     者：Yu Zhang Mingkui Zhang Jitao Li Guangshu Chen 

作者机构：School of Electrical and Information EngineeringBeijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big DataBeijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for GeoMechanics and Deep Underground EngineeringChina University of Mining&TechnologyBeijing100083China 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第47卷第11期

页      面：1987-2006页

学科分类：081901[工学-采矿工程] 0819[工学-矿业工程] 08[工学] 

基　　金：funded by State Key Laboratory for GeoMechanics and Deep Underground Engineering&Institute for Deep Underground Science and Engineering,Grant Number XD2021021 BUCEA Post Graduate Innovation Project under Grant,Grant Number PG2023092 

主　　题：Rockburst prediction rockburst intensity grade deep neural network batch gradient descent multi-scale residual 

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 *** rockburst only peels off rock slices without ejection,while severe rockburst causes casualties and property *** frequency and degree of rockburst damage increases with the excavation ***,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 ***,the prediction of rockburst intensity grade is one problem that needs to be solved *** comprehensively considering the occurrence mechanism of rockburst,this paper selects the stress index(σθ/σc),brittleness index(σ_(c)/σ_(t)),and rock elastic energy index(Wet)as the rockburst evaluation indexes through the Spearman coefficient *** overcomes the low accuracy problem of a single evaluation index prediction *** this,the BGD-MSR-DNN rockburst intensity grade prediction model based on batch gradient descent and a multi-scale residual deep neural network is *** 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 ***,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 *** experimental results reveal the BGDMSR-DNN model accuracy to reach 97.1%,outperforming other comparable ***,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.