Rockburst prediction and prevention in underground space excavation

The technical challenges associated with deep underground space activities have become increasingly significant.Among these challenges,one major concern is the assessment of rockburst risks and the instability of rock masses.Extensive research has been conducted by numerous scholars to mitigate the risks and prevent occurrences of rockburst through various assessment methods.Rockburst incidents commonly occur during the excavation of hard rock in underground environments,posing severe threats to personnel safety,equipment integrity,and operational continuity.Thus,it is crucial to systematically document real cases of rockburst,allowing for a comprehensive understanding of the underlying mechanisms and triggering conditions.This understanding will contribute to the advancement of rockburst prediction and prevention methods.Proper selection of an appropriate rockburst assessment method is a fundamental aspect in underground operations.However,there is a limited number of studies that summarize and compare different prediction and prevention methods of rockburst.This paper aims to address this gap by analyzing global trends using CiteSpace software since 1990.It discusses rockburst classification and characteristics,comprehensively reviews research findings related to rockburst prediction,including empirical,simulation,mathematical modeling,and microseismic monitoring methods.Additionally,the paper presents a compilation of current rockburst prevention measures.Notably,the paper emphasizes the significance of control strategies,which provide key insights into the effective utilization of stored energy within rock.Finally,the paper concludes by suggesting six directions for implementing intelligent management techniques to mitigate hazards during underground operations and reduce the probability of rockburst incidents.

Rockburst prediction using artificial intelligence techniques:A review

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation process.Rockburst disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict rockburst.Traditional rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a dilemma.With the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst mechanism.In previous research,several scholars have attempted to summarize the application of AI techniques in rockburst prediction.However,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive overview.Drawing on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI tech-niques.Firstly,pertinent definitions of rockburst and its associated hazards are introduced.Subsequently,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each approach.Finally,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

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.

Seismological method for prediction of areal rockbursts in deep mine with seismic source mechanism and unstable failure theory

The research on the rock burst prediction was made on the basis of seismology,rock mechanics and the data from Dongguashan Copper Mine(DCM) ,the deepest metal mine in China.The seismic responses to mining in DCM were investigated through the analyses of the spatio-temporal distribution of hypocenters,apparent stress and displacement of seismic events,and the process of the generation of hazardous seismicity in DCM was studied in the framework of the theory of asperity in the seismic source mechanism.A method of locating areas with hazardous seismicity and a conceptual model of hazardous seismic nucleation in DCM were proposed.A criterion of rockburst prediction was analyzed theoretically in the framework of unstable failure theories,and consequently,the rate of change in the ratio of the seismic stiffness of rock in a seismic nucleation area to that in surrounding area,dS/dt,is defined as an index of the rockburst prediction.The possibility of a rockburst will increase if dS/dt>0,and the possibility of rock burst will decrease if dS/dt<0.The correctness of these methods is demonstrated by analyses of rock failure cases in DCM.

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%).

Novel stacking models based on SMOTE for the prediction of rockburst grades at four deep gold mines

Rockburst is a frequently encountered hazard during the production of deep gold mines.Accurate prediction of rockburst is an important measure to prevent rockburst in gold mines.This study considers seven indicators to evaluate rockburst at four deep gold mines.Field research and rock tests were performed at two gold mines in China to collect these seven indicators and rockburst cases.The collected database was oversampled by the synthetic minority oversampling technique(SMOTE)to balance the categories of rockburst datasets.Stacking models combining tree-based models and logistic regression(LR)were established by the balanced database.Rockburst datasets from another two deep gold mines were implemented to verify the applicability of the predictive models.The stacking model combining extremely randomized trees and LR based on SMOTE(SMOTE-ERT-LR)was the best model,and it obtained a training accuracy of 100%and an evaluation accuracy of 100%.Moreover,model evaluation suggested that SMOTE can enhance the prediction performance for weak rockburst,thereby improving the overall performance.Finally,sensitivity analysis was performed for SMOTE-ERT-LR.The results indicated that the SMOTE-ERT-LR model can achieve satisfactory performance when only depth,maximum tangential stress index,and linear elastic energy index were available.

Evolution of anisotropy during sandstone rockburst process under double-faces unloading

Rockburst is one of the major disasters in deep underground rock mechanics and engineering.The precursors of rockbursts play important roles in rockburst prediction.Strainburst experiments were performed under double-face unloading on sandstone with horizontal bedding planes using an independently designed rockburst testing facility.P-wave propagation time during the tests was automatically recorded by the acoustic emission apparatus.The P-wave velocities were calculated in both two directions to analyze their patterns.To find a characteristic precursor for rockburst,the dynamic evolution of rock anisotropy during the rockburst test is quantified by the anisotropic coefficient k,defined as the ratio of the two P-wave velocities in the directions vertical to and parallel to the bedding planes.The results show that rockburst occurs on the two free surfaces asynchronously.The rockburst failure occurs in the following order:crack generation,rock peeling,particle ejection,and rock fracture.In the process of rockburst under double-face unloading,the potential evolution characteristics of anisotropy can be generalized as anisotropy-isotropy-anisotropy.The suddenly unloading induces damage in the rock and presents anisotropic coefficient k steeply increasing departing from one,i.e.,isotropy.The rocks with horizontal bedding planes will reach the isotropic state before rockburst,which could be considered as a characteristic precursor of this kind of rockburst.

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.