Rockburst criterion and evaluation method for potential rockburst pit depth considering excavation damage effect

Excavation-induced disturbances in deep tunnels will lead to deterioration of rock properties and formation of excavation damaged zone(EDZ).This excavation damage effect may affect the potential rockburst pit depth.Taking two diversion tunnels of Jinping II hydropower station for example,the relationship between rockburst pit depth and excavation damage effect is first surveyed.The results indicate that the rockburst pit depth in tunnels with severe damage to rock masses is relatively large.Subsequently,the excavation-induced damage effect is characterized by disturbance factor D based on the Hoek-Brown criterion and wave velocity method.It is found that the EDZ could be further divided into a high-damage zone(HDZ)with D=1 and weak-damage zone(WDZ),and D decays from one to zero linearly.For this,a quantitative evaluation method for potential rockburst pit depth is established by presenting a three-element rockburst criterion considering rock strength,geostress and disturbance factor.The evaluation results obtained by this method match well with actual observations.In addition,the weakening of rock mass strength promotes the formation and expansion of potential rockburst pits.The potential rockburst pit depth is positively correlated with HDZ and WDZ depths,and the HDZ depth has a significant contribution to the potential rockburst pit depth.

A review of rockburst:Insights from engineering sites to theoretical investigations

Rockburst has perennially posed a formidable challenge to the stability of underground engineering works,particularly under conditions of deep-seated high stress.This paper provides a comprehensive review of recent advancements in on-site research related to rockburst occurrences,covering on-site case analyses,monitoring methodologies,early warning systems,and risk(proneness)evaluation.Initially,the concepts and classifications of rockburst based on on-site understanding were summarized.The influences of structural planes(in various spatial distribution combinations),in-situ stress(particularly magnitude and direction of the principal stress),dynamic disturbances,and excavation profiles on rockburst were thoroughly assessed and discussed through the analysis of published rockburst cases and on-site survey results.Subsequently,a compendium of commonly employed on-site monitoring techniques was outlined,delineating their respective technical attributes.Particular emphasis is accorded to the efficacy of microseismic monitoring technology and its prospective utility in facilitating dynamic rockburst early warning mechanisms.Building upon this foundation,the feasibility of assessing rockburst propensity while considering on-site variables is verified,encompassing the selection and quantitative evaluation of pertinent indicators.Ultimately,a comprehensive synthesis of the paper is presented,alongside the articulation of prospective research goals for the future.

Research on the mechanism of rockburst induced by mined coal-rock linkage of sharply inclined coal seams

In recent years,the mining depth of steeply inclined coal seams in the Urumqi mining area has gradually increased.Local deformation of mining coal-rock results in frequent rockbursts.This has become a critical issue that affects the safe mining of deep,steeply inclined coal seams.In this work,we adopt a perspective centered on localized deformation in coal-rock mining and systematically combine theoretical analyses and extensive data mining of voluminous microseismic data.We describe a mechanical model for the urgently inclined mining of both the sandwiched rock pillar and the roof,explaining the mechanical response behavior of key disaster-prone zones within the deep working face,affected by the dynamics of deep mining.By exploring the spatial correlation inherent in extensive microseismic data,we delineate the“time-space”response relationship that governs the dynamic failure of coal-rock during the progression of the sharply inclined working face.The results disclose that(1)the distinctive coal-rock occurrence structure characterized by a“sandwiched rock pillar-B6 roof”constitutes the origin of rockburst in the southern mining area of the Wudong Coal Mine,with both elements presenting different degrees of deformation localization with increasing mining depth.(2)As mining depth increases,the bending deformation and energy accumulation within the rock pillar and roof show nonlinear acceleration.The localized deformation of deep,steeply inclined coal-rock engenders the spatial superposition of squeezing and prying effects in both the strike and dip directions,increasing the energy distribution disparity and stress asymmetry of the“sandwiched rock pillar-B3+6 coal seam-B6 roof”configuration.This makes worse the propensity for frequent dynamic disasters in the working face.(3)The developed high-energy distortion zone“inner-outer”control technology effectively reduces high stress concentration and energy distortion in the surrounding rock.After implementation,the average apparent resistivity in the rock pillar and B6 roof substantially increased by 430%and 300%,respectively,thus guaranteeing the safe and efficient development of steeply inclined coal seams.

Classifying rockburst with confidence:A novel conformal prediction approach

The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence value.Given the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential failures.To address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its output.The proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test dataset.The CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence levels.Significantly,the CP identified several“confident”classifications from the traditional ML model as unreliable,necessitating expert verification for informed decision-making.The proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.

Experimental study on the effect of water absorption level on rockburst occurrence of sandstone

To investigate the mechanism of rockburst prevention by spraying water onto the surrounding rocks,15 experiments are performed considering different water absorption levels on a single face.High-speed photography and acoustic emission(AE)system are used to monitor the rockburst process.The effect of water on sandstone rockburst and the prevention mechanism of water on sandstone rockburst are analyzed from the perspective of energy and failure mode.The results show that the higher the ab-sorption degree,the lower the intensity of the rockburst after absorbing water on single side of sand-stone.This is reflected in the fact that with the increase in the water absorption level,the ejection velocity of rockburst fragments is smaller,the depth of the rockburst pit is shallower,and the AE energy is smaller.Under the water absorption level of 100%,the magnitude of rockburst intensity changes from medium to slight.The prevention mechanism of water on sandstone rockburst is that water reduces the capacity of sandstone to store strain energy and accelerates the expansion of shear cracks,which is not conducive to the occurrence of plate cracking before rockburst,and destroys the conditions for rockburst incubation.

Dynamic response mechanism and precursor characteristics of gneiss rockburst under different initial burial depths

To investigate the influence mechanism of geostress on rockburst characteristics,three groups of gneiss rockburst experiments were conducted under different initial geostress conditions.A high-speed photography system and acoustic emission(AE)monitoring system were used to monitor the entire rockburst process in real time.The experimental results show that when the initial burial depth increases from 928 m to 1320 m,the proportion of large fracture scale in rockburst increases by 154.54%,and the AE energy increases by 565.63%,reflecting that the degree and severity of rockburst increase with the increase of burial depth.And then,two mechanisms are proposed to explain this effect,including(i)the increase of initial geostress improves the energy storage capacity of gneiss,and then,the excess energy which can be converted into kinetic energy of debris ejection increases,consequently,a more pronounced violent ejection phenomenon is observed at rockburst;(ii)the increase of initial geostress causes more sufficient plate cracks of gneiss after unloading ofσh,which provides a basis for more severe ejection of rockburst.What’s more,a precursor with clear physical meaning for rockburst is proposed under the framework of dynamic response process of crack evolution.Finally,potential value in long term rockburst warning of the precursor obtained in this study is shown via the comparison of conventional precursor.

Experimental study on the effect of unloading rate on gneiss rockburst

Rockburst are often encountered in tunnel construction due to the complex geological conditions.To study the influence of unloading rate on rockburst,gneiss rockburst experiments were conducted under three groups of unloading rates.A high-speed photography system and acoustic emission(AE)system were used to monitor the entire process of rockburst process in real-time.The results show that the intensity of gneiss rockburst decreases with decrease of unloading rate,which is manifested as the reduction of AE energy and fragments ejection velocity.The mechanisms are proposed to explain this effect:(i)The reduction of unloading rate changes the crack propagation mechanism in the process of rockburst.This makes the rockbursts change from the tensile failure mechanism at high unloading rate to the tension-shear mixed failure mechanism at low unloading rate,and more energy released in the form of shear crack propagation.Then,less strain energy is converted into kinetic energy of fragments ejection.(ii)Less plate cracking degree of gneiss has taken shape due to decrease of unloading rate,resulting in the destruction of rockburst incubation process.The enlightenments of reducing the unloading rate for the project are also described quantitatively.The rockburst magnitude is reduced from the medium magnitude at the unloading rate of 0.1 MPa/s to the slight magnitude at the unloading rate of 0.025 MPa/s,which was judged by the ejection velocity.

Influence of axial stress on rockburst in deep tunnels: insight from model experiment

Frequent rockburst disasters in deep-buried engineering projects severely impact construction. To explore the influence of axial stress on rockburst in deep-buried tunnels, large-scale true triaxial rockburst experiments were conducted under four different axial stress ratio conditions (ηt, axial loading stress/vertical loading stress) using a self-developed true triaxial loading device under the condition of "pre-loading before excavation". The influence of axial stress on the rockburst process and failure characteristics in deep tunnels was studied using a combination of real-time video monitoring, rockburst debris sieving, and acoustic emission monitoring. The results indicate: (1) all four specimens subjected to different axial stress ratio loading conditions exhibited three stages of macroscopic failure: small particle ejection, flake spalling, and large fragment ejection. Ultimately, "V"-shaped notches appeared on both sides of the tunnel. (2) The failure stress, fragment volume, and fragment size distribution of the rockburst specimens exhibited a clear two-stage failure characteristic with increasing axial stress ratio. In the lower axial stress ratio stage (ηt ≤ 0.7), the increase in the axial stress ratio enhances lateral confinement, thereby increasing the crack initiation strength of the surrounding rock, inhibiting crack formation and propagation, and thus suppressing damage to the surrounding rock of the tunnel. In the higher axial stress ratio stage (ηt > 0.7), the increase in axial stress ratio makes the Poisson effect of the surrounding rock more pronounced, promoting the generation and propagation of cracks along the tunnel axis direction, thereby promoting damage to the surrounding rock. (3) Based on the analysis of acoustic emission parameters (fracture properties), it can be concluded that in the lower axial stress ratio stage (ηt ≤ 0.7), an increase in the axial stress ratio leads to a higher proportion of shear fracture in rockburst damage. Conversely, in the higher axial stress ratio stage (ηt > 0.7), the increase in axial stress ratio gradually reduces the proportion of shear fracture in rockburst damage.

A method to predict rockburst using temporal trend test and its application

Rockbursts have become a significant hazard in underground mining,underscoring the need for a robust early warning model to ensure safety management.This study presents a novel approach for rockburst prediction,integrating the Mann-Kendall trend test(MKT)and multi-indices fusion to enable real-time and quantitative assessment of rockburst hazards.The methodology employed in this study involves the development of a comprehensive precursory index library for rockbursts.The MKT is then applied to analyze the real-time trend of each index,with adherence to rockburst characterization laws serving as the warning criterion.By employing a confusion matrix,the warning effectiveness of each index is assessed,enabling index preference determination.Ultimately,the integrated rockburst hazard index Q is derived through data fusion.The results demonstrate that the proposed model achieves a warning effectiveness of 0.563 for Q,surpassing the performance of any individual index.Moreover,the model’s adaptability and scalability are enhanced through periodic updates driven by actual field monitoring data,making it suitable for complex underground working environments.By providing an efficient and accurate basis for decision-making,the proposed model holds great potential for the prevention and control of rockbursts.It offers a valuable tool for enhancing safety measures in underground mining operations.

Risk assessment of rockburst using SMOTE oversampling and integration algorithms under GBDT framework

Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.

Rockburst proneness considering energy characteristics and sample shape effects

Accurate prediction of rockburst proneness is one of challenges for assessing the rockburst risk and selecting effective control measures.This study aims to assess rockburst proneness by considering the energy characteristics and qualitative information during rock failure.Several representative rock types in cylindrical and cuboidal sample shapes were tested under uniaxial compression conditions and the failure progress was detected by a high-speed camera.The far-field ejection mass ratio(FEMR)was determined considering the qualitative failure information of the rock samples.The peak-strength energy impact index and the residual elastic energy index were used to quantitatively evaluate the rockburst proneness of both cylindrical and cuboidal samples.Further,the performance of these two indices was analyzed by comparing their estimates with the FEMR.The results show that the accuracy of the residual elastic energy index is significantly higher than that of the peak-strength energy impact index.The residual elastic energy index and the FEMR are in good agreement for both cylindrical and cuboidal rock materials.This is because these two indices can essentially reflect the common energy release mechanism characterized by the mass,ejection velocity,and ejection distance of rock fragments.It suggests that both the FEMR and the residual elastic energy index can be used to accurately measure the rockburst proneness of cylindrical and cuboidal samples based on uniaxial compression test.

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.

A review of rockburst:Experiments,theories,and simulations

Rockburst is becoming a huge challenge for the utilization of deep underground space.Extensive efforts have been devoted to investigating the rockburst behavior and mechanism experimentally,theoretically,and numerically.The aim of this review is to discuss the novel development and the state-of-the-art in experimental techniques,theories,and numerical approaches proposed for rockburst.The definition and classification of rockburst are first summarized with an in-depth comparison among them.Then,the available laboratory experimental technologies for rockburst are reviewed in terms of indirect and direct approaches,with the highlight of monitoring technologies and data analysis methods.Some key rockburst influencing factors(i.e.size and shape,rock types,stress state,water content,and temperature)are analyzed and discussed based on collected data.After that,rockburst theories and mechanisms are discussed and evaluated,as well as the microscopic observation.The simulation approaches of rockburst are also summarized with the highlight of optional novel numerical methods.The accuracy,stability,and reliability of different experimental,theoretical and numerical approaches are also compared and assessed in each part.Finally,a summary and some aspects of prospective research are presented.

Rockburst process and strength-weakening effect of the high-stress circular tunnel under internal unloading

To investigate the influence of unloading effect of a circular tunnel face on rockburst process,by innovatively combining rock drilling unloading devices and triaxial systems,the strain rockburst simulation under the entire stress path of“high initial stressþinternal unloadingþstress adjustment”(HUS test)was realized for the intact cubic red sandstone samples(100 mm×100 mm×100 mm).Comparative tests were conducted on cubic red sandstone samples with prefabricated circular holes(425 mm)under the stress path of“prefabricated circular hole+þhigh initial stress+stress adjustment”(PHS test),thereby highlighting the influence of internal unloading on rockburst failure.The test results revealed that with an increase in vertical stress,the sidewalls in both the HUS and PHS tests suffered strain rockburst failure.Compared with the PHS test,the initial failure stress in the HUS test is lower,and it is easier to induce sidewall rockbursts.This indicates that the internal unloading influences the sidewall failure,causing an obvious strength-weakening effect,which becomes more significant with an increase in buried depth.The strain rockburst failure was more severe in the HUS test owing to the influence of internal unloading.V-shaped rockburst pits were formed in the HUS tests,whereas in the PHS test,arcshaped rockburst pits were produced.It was also found that strain rockburst failure may occur only when the rock has a certain degree of rockburst proneness.

Analysis of rockburst mechanism and warning based on microseismic moment tensors and dynamic Bayesian networks

One of the major factors inhibiting the construction of deep underground projects is the risk posed by rockbursts.A study was conducted on the access tunnel of the Shuangjiangkou hydropower station to determine the evolutionary mechanism of microfractures within the surrounding rock mass during rockburst development and develop a rockburst warning model.The study area was chosen through the combination of field studies with an analysis of the spatial and temporal distribution of microseismic(MS)events.The moment tensor inversion method was adopted to study rockburst mechanism,and a dynamic Bayesian network(DBN)was applied to investigating the sensitivity of MS source parameters for rockburst warnings.A MS multivariable rockburst warning model was proposed and validated using two case studies.The results indicate that fractures in the surrounding rock mass during the development of strain-structure rockbursts initially show shear failure and are then followed by tensile failure.The effectiveness of the DBN-based rockburst warning model was demonstrated using self-validation and K-fold cross-validation.Moment magnitude and source radius are the most sensitive factors based on an investigation of the influence on the parent and child nodes in the model,which can serve as important standards for rockburst warnings.The proposed rockburst warning model was found to be effective when applied to two actual projects.

Numerical simulation of rockburst disaster and control strategy of constant resistance and large deformation anchor cable in Gaoloushan tunnel

The Gaoloushan Tunnel in Longnan City,Gansu Province,China,frequently experiences rockburst disasters due to high in-situ stress.Managing rockburst in deep-buried tunnels remains a challenging issue.This paper employs RFPA(Rock Failure Process Analysis)software to establish a calculation model of constant resistance and large deformation(CRLD)anchorages and analyzes the effects of different support methods and pre-stress levels on rockburst.We simulate the process of tunnel rockburst disasters and find that ordinary anchor support incurs rockburst on the right arch waist and arch top,forming a V-shaped explosion pit.CRLD anchor support has several advantages in rockburst control,such as more uniform stress distribution in the surrounding rock,a uniform distribution of plastic zones,less noticeable damage to the tunnel,and effective control of the arch top displacement.The effectiveness of the CRLD anchor support under varying pre-stress conditions shows that a higher prestress results in a smaller plastic zone of the surrounding rock and arch top displacement and a lower number of acoustic emission signals,which better explains the excavation compensation effect.Moreover,adding long anchorages in the deep surrounding rock area can better control rockburst and reduce surrounding rock deformation.Based on these findings,we propose a comprehensive control system that combines long and short anchorages and provides the optimal scheme based on calculations.Therefore,by using high-prestress CRLD anchor support and the combination of long and short anchorages at critical positions,we can enhance the integrity of the surrounding rock,effectively absorb the energy released by the surrounding rock deformation,and reduce the incidence of rockburst disasters.

Rockburst proneness criteria for rock materials:Review and new insights

To review the rockburst proneness(or tendency)criteria of rock materials and compare the judgment accuracy of them,twenty criteria were summarized,and their judgment accuracy was evaluated and compared based on the laboratory tests on fourteen types of rocks.This study begins firstly by introducing the twenty rockburst proneness criteria,and their origins,definitions,calculation methods and grading standards were summarized in detail.Subsequently,to evaluate and compare the judgment accuracy of the twenty criteria,a series of laboratory tests were carried out on fourteen types of rocks,and the rockburst proneness judgment results of the twenty criteria for the fourteen types of rocks were obtained accordingly.Moreover,to provide a unified basis for the judgment accuracy evaluation of above criteria,a classification standard(obtained according to the actual failure results and phenomena of rock specimen)of rockburst proneness in laboratory tests was introduced.The judgment results of the twenty criteria were compared with the judgment results of this classification standard.The results show that the judgment results of the criterion based on residual elastic energy(REE)index are completely consistent with the actual rockburst proneness,and the other criteria have some inconsistent situations more or less.Moreover,the REE index is based on the linear energy storage law and defined in form of a difference value and considered the whole failure process,and these superior characteristics ensure its accuracy.It is believed that the criterion based on REE index is comparatively more accurate and scientific than other criteria,and it can be recommended to be applied to judge the rockburst proneness of rock materials.

A comprehensive geomechanical method for the assessment of rockburst hazards in underground mining

Rockburst hazard in mining industry all over the world is one of the most severe hazards. It is becoming increasingly common because of the ever-growing depths of mining operations accompanied by the increasing strength of rocks. One of the most difficult issues is to predict this hazard before the mining operations, whether geophysical investigations have been conducted or not. Polish experience in this field shows that in such cases an effective solution can be the geomechanical method. Therefore, extensive studies on rockburst hazard should focus on three main aspects:(1) rock mass and rock(and coal)predisposition to rockburst–laboratory tests and empirical analyses based on lithology,(2) identification of the potential places with stress and elastic energy concentration in the rock mass within the area planned for exploitation, and(3) the assessment of the impact of mining tremors on the surface. This preliminary geomechanical analysis assesses the propensity of the rock mass to dynamic breakage and provides quantitatively the level of rockburst hazard. The paper presents Polish experience in rockburst hazard assessment with the use of geomechanical method, as well as some solutions and examples of such analyses.

Prediction of rockburst classification using Random Forest

The method of Random Forest （RF） was used to classify whether rockburst will happen and the intensity of rockburst in the underground rock projects. Some main control factors of rockburst, such as the values of in-situ stresses, uniaxial compressive strength and tensile strength of rock, and the elastic energy index of rock, were selected in the analysis. The traditional indicators were summarized and divided into indexes I and 1I. Random Forest model and criterion were obtained through training 36 sets of rockburst samples which come from underground rock projects in domestic and abroad. Another 10 samples were tested and evaluated with the model. The evaluated results agree well with the practical records. Comparing the results of support vector machine （SVM） method, and artificial neural network （ANN） method with random forest method, the corresponding misjudgment ratios are 10%, 20%, and 0, respectively. The misjudgment ratio using index I is smaller than that using index II. It is suggested that using the index I and RF model can accurately classify rockburst grade.

Spatial structure evolution of overlying strata and inducing mechanism of rockburst in coal mine

Spatial structure of overlying strata existed and evolved dynamically with the exploitation scope （boundary conditions） changes in coal mines and to induce rockburst. Based on the“key strata”theory, the integrated spatial structure of overlying strata was put forward, which was composed of “O-X” structure in the plane section and “F” structure in the vertical section. The formation and ongoing instability of the“O-X”and“F”structures were called as dynamic evolution cycle of the overlying strata. Three basic categories of “O-X”, “F” and “T” structures were defined, and the strata behaviors of each spatial structure were analyzed. According to energy theory, mechanism of rockburst induced by spatial structure instability was discussed. The research expanded the scope of traditional ground pressure theory and provided a guide for the prevention of rockburst and mining tremors induced by structure instability of overlying