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.

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.

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 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.

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.

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.

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.

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 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.

冲击地压多参量智能化监测分析及预警研究

针对张双楼煤矿冲击地压成因复杂、预警监测单一造成的预警准确率低的问题,基于冲击地压智能化预警平台,对张双楼煤矿冲击地压现状及监测预警效能进行分析。在智慧矿山理念背景下,以“应力场-能量震动场-变形场”为监测预警框架,从引发冲击地压的内因和外因两个维度出发,提出了以应力、震动波、钻屑量和地音值为参量的多参量监测预警方法,结合参量分析技术对冲击地压发生前各监测数据变化特征进行系统全面评价,为张双楼煤矿冲击地压预警与科学及时解危提供依据。现场应用结果表明,多参量监测预警方法的综合预警能力及准确度显著优于单一指标预警系统,为矿井安全开采和智能化矿井建设提供了参考借鉴价值。

岩爆机制及其控制

随着岩体工程向深部发展,开挖引发的围岩大变形和强冲击诱发的动力破坏日益严峻。岩爆作为深部地下工程中常见的一种强动力灾害,被称为岩土工程界的“癌症”,其具有突发性特点,难以有效预报及防治,严重地威胁着施工人员的安全和设备的正常运行。通过对比岩石在真三轴卸荷和单轴压缩这2种应力路径下的储能差异,对岩爆多余能量机制进行探讨。首先,根据岩爆诱发机制的不同,将岩爆分为由应力集中导致的应变型岩爆和扰动诱发的冲击岩爆。然后,模拟开挖诱发的不同类型岩爆的应力路径进而在室内再现各种岩爆现象,并对其机制进行研究,自主研发3套真三轴岩爆模拟试验系统:第1代应变岩爆试验系统、第2代应变岩爆试验系统、冲击岩爆试验系统。同时,在实验室内成功模拟不同类型的岩爆:巷道应变岩爆、巷道交叉点应变岩爆、3个临空面矿柱应变岩爆、矿柱应变岩爆以及动荷载诱发的冲击岩爆。最后,为有效控制岩爆灾害,提出开挖补偿法,并研发具有负泊松比效应的宏观和微观NPR锚杆/索。工程实践证明,NPR锚杆/索能提供高预应力来补偿开挖造成的应力损失,同时具备良好的延展性和吸能效果,可为岩爆灾害的防治提供有效途径。

井下断层活动的定量监测及其对冲击地压的影响研究

为建立断层活动与冲击地压之间的定量关系,实现煤矿冲击地压有效防控。通过理论分析义马矿区地质构造环境,以及耿村煤矿13200工作面与断层影响带的相对位置关系,研究了断裂构造对冲击地压的宏观控制作用。计算了井田构造应力并进行了构造应力区划分,分析了构造应力分区对冲击地压的控制作用。提出了井下断层活动性的定量监测方法,构建了“震源区煤岩体与动力核区尺度等量,震源能量随传递距离逐渐衰减”的模型,建立了大能量微震事件与断层活动的关系,确定了断层活动性对冲击地压的影响。研究结果表明:义马煤田内相对复杂的逆冲推覆构造体系,构成了义马矿区冲击地压的地质构造背景条件。F_(16)断层的影响带范围为7000~7600 m,13200工作面全部处于F_(16)断层的影响带内,在开采活动的影响下进一步增大了冲击地压的发生危险。Ⅰ-2断裂、Ⅲ-4断裂和Ⅳ-7断裂等控制的区域是冲击地压和大能量微震事件显现的主要区域,且冲击地压和大能量微震事件大多位于应力梯度区范围内。在大能量微震事件孕育和发生期间,F_(16)断层位移分别增长50 mm和45 mm;大能量微震事件发生前,断层活动拉力的增幅均相对最高,分别为2.58 kN和2.93 kN,断层位移量的快速增加和较高的应力增幅构成了大能量微震事件的主要能量来源。表明大能量微震事件和冲击地压的发生均与断层的活动联系紧密。井下断层的实际定量监测方法可以广泛应用于矿井冲击地压预测与防控的指导工作中。

基于D-S证据理论的岩爆预测方法研究

为了有效预测岩爆,提出基于D-S证据理论的岩爆预测方法.首先,选取与岩爆发生相关的6个指标因素作为证据体,并通过模糊物元框架和正态型隶属度函数构建证据体的基本概率分配.然后,利用K均值将证据体分类,并提出簇内证据用传统方式融合而簇间证据用权重方式融合的组合融合规则,以减轻高冲突证据融合的不利影响.最后,将模型应用在秦岭终南山公路隧道2号竖井工程,且与经验方法对比.为了分析预测过程的不确定性和估计岩爆发生概率,采用蒙特卡洛模拟进行抽样仿真,并通过Spearman秩相关系数衡量输入指标的全局敏感性.研究结果表明:输入指标在不同的岩爆案例的影响程度差异较大且方向不同;5个岩爆案例的发生概率在40.8%~70.1%之间.该模型表现出优异的预测分类性能,可为深埋地下工程岩爆预测提供参考.