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

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(σθ/σc),brittleness index(σ_(c)/σ_(t)),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 BGDMSR-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.

Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization

The rockburst prediction becomes more and more challenging due to the development of deep underground projects and constructions.Increasing numbers of intelligent algorithms are used to predict and prevent rockburst.This paper investigated the drawbacks of neural networks in rockburst prediction,and aimed at these shortcomings,Bayesian optimization and the synthetic minority oversampling technique+Tomek Link(SMOTETomek)were applied to efficiently develop the feedforward neural network(FNN)model for rockburst prediction.In this regard,314 real rockburst cases were collected to establish a database for modeling.The database was divided into a training set(80%)and a test set(20%).The maximum tangential stress,uniaxial compressive strength,tensile strength,stress ratio,brittleness ratio,and elastic strain energy were selected as input parameters.Bayesian optimization was implemented to find the optimal hyperparameters in FNN.To eliminate the effects of imbalanced category,SMOTETomek was adopted to process the training set to obtain a balanced training set.The FNN developed by the balanced training set received 90.48% accuracy in the test set,and the accuracy improved 12.7% compared to the imbalanced training set.For interpreting the FNN model,the permutation importance algorithm was introduced to analyze the relative importance of input variables.The elastic strain energy was the most essential variable,and some measures were proposed to prevent rockburst.To validate the practicability,the FNN developed by the balanced training set was utilized to predict rockburst in Sanshandao Gold Mine,China,and it had outstanding performance(accuracy 100%).

Rockburst in underground excavations:A review of mechanism,classification,and prediction methods

Technical challenges have always been part of underground mining activities,however,some of these challenges grow in complexity as mining occurs in deeper and deeper settings.One such challenge is rock mass stability and the risk of rockburst events.To overcome these challenges,and to limit the risks and impacts of events such as rockbursts,advanced solutions must be developed and best practices implemented.Rockbursts are common in underground mines and substantially threaten the safety of personnel and equipment,and can cause major disruptions in mine development and operations.Rockbursts consist of violent wall rock failures associated with high energy rock projections in response to the instantaneous stress release in rock mass under high strain conditions.Therefore,it is necessary to develop a good understanding of the conditions and mechanisms leading to a rockburst,and to improve risk assessment methods.The capacity to properly estimate the risks of rockburst occurrence is essential in underground operations.However,a limited number of studies have examined and compared yet different empirical methods of rockburst.The current understanding of this important hazard in the mining industry is summarized in this paper to provide the necessary perspective or tools to best assess the risks of rockburst occurrence in deep mines.The various classifications of rockbursts and their mechanisms are discussed.The paper also reviews the current empirical methods of rockburst prediction,which are mostly dependent on geomechanical parameters of the rock such as uniaxial compressive strength of the rock,as well as its tensile strength and elasticity modulus.At the end of this paper,some current achievements and limitations of empirical methods are discussed.