Search Results (37)

Precise forecasting of rockburst intensity categories is vital to safeguarding operational safety and refining design protocols in deep underground engineering. This study proposes an intelligent forecasting framework through the integration of k-medoids-SMOTE and the BSLO-optimized Random Forest (BSLO-RF) algorithm. A curated dataset encompassing 351 rockburst instances, stratified into four intensity grades, was compiled via systematic literature synthesis. To mitigate data imbalance and outlier interference, z-score normalization and k-medoids-SMOTE oversampling were implemented, with t-SNE visualization confirming improved inter-class distinguishability. Notably, the BSLO algorithm was utilized for hyperparameter tuning of the Random Forest model, thereby strengthening its global search and local refinement capabilities. Comparative analyses revealed that the optimized BSLO-RF framework outperformed conventional machine learning methods (e.g., BSLO-SVM, BSLO-BP), achieving an average prediction accuracy of 89.16% on the balanced dataset—accompanied by a recall of 87.5% and F1-score of 0.88. It exhibited superior performance in predicting extreme grades: 93.3% accuracy for Level I (no rockburst) and 87.9% for Level IV (severe rockburst), exceeding BSLO-SVM (75.8% for Level IV) and BSLO-BP (72.7% for Level IV). Field validation via the Zhongnanshan Tunnel project further corroborated its reliability, yielding an 80% prediction accuracy (four out of five cases correctly classified) and verifying its adaptability to complex geological settings. This research introduces a robust intelligent classification approach for rockburst intensity, offering actionable insights for risk assessment and mitigation in deep mining and tunneling initiatives. Full article

The Qinghai–Xizang Plateau and its surrounding regions have experienced intense tectonic activity, resulting in complex geostress environments that cause frequent and distinctive rockburst disasters in plateau tunnel engineering. In this study, numerical simulations were conducted to investigate the distribution characteristics and patterns of tunnel rockbursts in high-altitude regions, using geostress orientation, lateral pressure coefficient, and tunnel depth as the primary independent variables. Secondary development of FLAC3D 7.00.126 was carried out using FISH language to enable the recording and visualization of tangential stress, the Russense rockburst criterion, and elastic strain energy. Based on this, the influence mechanisms of these key geostress parameters on the location, extent, and intensity of rockbursts within tunnel cross sections were analyzed. Results indicate that geostress orientation predominantly affects the location of rockbursts, with the surrounding rock in the direction of the minimum principal stress on the tunnel cross section being particularly prone to rockburst risks. The lateral pressure coefficient primarily influences the rockburst intensity and pit range within local stress concentration zones, with higher values leading to greater rockburst intensity. Notably, when structural stress is sufficiently large, rockbursts may occur even in tunnels with shallow burial depths. Tunnel depth determines the magnitude of geostress, mainly affecting the overall risk and potential extent of rockbursts within the cross section, with greater depths leading to higher rockburst intensities and a wider affected area. Full article

The construction industry is undergoing a transformative shift through automation, with advancements in Generative AI (GenAI) and prompt engineering enhancing safety and efficiency, particularly in high-risk fields like underground construction, geotechnics, and mining. In underground construction, GenAI-powered prompts are revolutionizing practices by enabling a shift from reactive to predictive approaches, leading to advancements in design, project planning, and site management. This study explores the use of Google Gemini, a recent advancement in GenAI, for the prediction of rockburst intensity levels in underground construction. The Python programming language and the Google Gemini tool are combined with prompt engineering to generate prompts that incorporate essential variables related to rockburst. A comprehensive database of 93 documented rockburst cases is compiled. Subsequently, a systematic method is established that involves the categorization of intensity levels through data visualization and factor analysis in order to identify a reduced number of unobservable underlying factors. Furthermore, K-means clustering is utilized to identify data patterns. The gradient boosting classifier is then employed to predict the intensity levels of rockburst. The results demonstrate that GenAI and prompt engineering offers an effective approach for accurately predicting rockburst events, achieving an accuracy rate of 89 percent. Through predictive modeling with GenAI, construction engineering experts can proactively evaluate the likelihood of rockburst, allowing for improved risk management, optimized excavation strategies, and enhanced safety protocols. This approach enables the automation of complex analyses and provides a powerful tool for real-time decision-making and predictive insights, offering significant benefits to industries reliant on underground construction. However, despite the considerable potential of GenAI and prompt engineering in the construction sector, challenges related to output accuracy, the dynamic nature of projects, and the need for human oversight must be carefully addressed to ensure effective implementation. Full article

In terms of rockburst control technology, it is generally believed that optimizing the section design and adopting the step method can effectively suppress the occurrence of rockburst, but there is no literature to explain the reasons for adopting this method from the experimental point of view. In addition, compared with the application of support, this method can achieve the effect of not increasing the construction process, not affecting the progress of the project and reducing the project cost. In view of this, the Gaoloushan deep-buried tunnel with rockburst was taken as the research object in this paper. Firstly, the excavation scheme based on the step method was proposed, and its explosion-proof effect was verified again. The experimental results showed that the step method could be essentially regarded as the transformation of surrounding rock by reasonably distributing explosives and reducing the working section. The beneficial effects of this method were as follows: the release intensity of absolute energy was slowed down, the way of energy release was changed; the stress condition of surrounding rock was improved; the path of the continuous supplement of strain energy in the original rockburst area was cut off; and the energy accumulation degree of surrounding rock was reduced, so that the accumulated energy in the rock mass did not exceed its energy storage limit at the location where the rockburst should have occurred. The reduced high energy was released in an orderly manner and induced the rock failure process, forming a fracture zone and a plastic zone. In the process of expansion, the fracture zone and plastic zone further reduced the stress concentration of the surrounding rock and deteriorated the mechanical properties of the surrounding rock. The stress concentration zone was transferred to the deeper surrounding rock outside the unloading relaxation zone, and part of the elastic energy accumulated in the surrounding rock was released. The strain energy could be distributed and dissipated, and the effect of energy safety and slow release was achieved. Full article

Coal seam gas drainage serves as an effective engineering measure to mitigate compound disasters of the rockburst and outburst in deep mining, and its efficacy is fundamentally governed by the permeability of coal around the gas drainage borehole. To systematically study the permeability law of broken coal body around borehole under different stress states and particle size distribution, the coal particle samples were prepared for the triaxial permeability tests by the gradation theory whose Talbot power exponents n are 0.1 to 1.0. Several valuable findings have been obtained through meticulous research and analysis, according to Darcy’s law and the Forchheimer equation. The seepage velocity is affected by the Talbot power exponent, pressure gradient, confining pressure, and axial pressure, among which the pressure gradient has the most prominent influence. The larger the Talbot power exponent of the sample composition and the larger of the pressure gradient inside the sample, the larger is the seepage velocity obtained by the sample. The axial pressure has a notable influence on permeability by modifying the pore structure of broken coal. As the axial pressure increases, the permeability decays exponentially until it reaches a stable state at a specific limit. The permeability decreases exponentially with the increase of effective stress, while the power exponent (a) decreases gradually with the increase of Talbot power exponent, and the coefficient (b) increases gradually with the increase of Talbot power exponent (index), in the effective stress-permeability equation, which implies that the inhibition and amplitude effects of effective stress on permeability become more intense. The permeability shows three stages of growth, namely the slow growth stage, the linear growth stage, and the exponential growth stage, which are influenced by small-sized coal particles, particle-size ratio, and large-sized coal particles respectively, when the Talbot power exponent (n) of the broken coal increases from 0.1 to 1.0. These findings advance understanding of the permeability of broken coal around boreholes, providing theoretical foundations for optimizing gas drainage parameters and preventing the compound disaster of the rockburst and outburst. Full article

The use of tunnel boring machines (TBMs) in deep hard rock tunnels disrupts the original stress equilibrium of the rock mass, often resulting in the aggregation and release of a large amount of elastic strain energy, and even leading to rockburst. Under extremely high rockburst proneness conditions, advance stress release boreholes (ASRBs) deployed behind the TBM cutter head can be used to reduce stress concentration levels. However, there is a lack of scientific design methods for the parameters of the ASRB program for TBM tunnels, leading to poor stress release and difficulty in mitigating high-intensity rockburst hazards. This study proposes a parameter design method for ASRBs in the potential rockburst seismic source area of deeply buried hard rock TBM tunnels, including test scheme establishment methods, parameter selection methods, and parameter space relationship and evaluation index establishment methods. A deep tunnel in southwest China was used as an engineering case study to explore the effect of stress release and energy dissipation under different ASRB layout schemes. The results show that the sensitivity of the five important design parameters of ASRBs to the stress release effect is, in descending order, “aperture”, “inclination”, “included angle”, “spacing”, and “length”. A parameter control law for ASRBs is proposed, which confirms their effectiveness in preventing, controlling, and reducing rockburst disasters in deep hard rock TBM tunnels. Full article

Influenced by various factors such as the complex environment and high key layers in coal mines, hydraulic fracturing technology has gradually become the main means of controlling the hard roof strata to prevent and control rockburst in recent years, which can effectively release the stress on the roof, reduce the intensity of pressure, and ensure the safe and efficient mining of the working face in coal mines. However, the current research on hydraulic fracturing to prevent and control rockburst is mostly limited to optimizing fracturing parameters and monitoring and evaluating fracturing effects, and there are few studies on blank sections, which cannot guarantee the overall prevention and control effect of rockburst, or increase unnecessary construction costs. In this paper, for the directional long borehole staged hydraulic fracturing project, triangular-type blank sections and regular-type blank sections are defined, and the rockburst prevention and control effects of fracturing sections and triangular-type blank sections during fracturing are compared and analyzed by the underground–ground integrated microseismic monitoring technology and transient electromagnetic detection technology, and the rockburst prevention and control effects of fracturing sections and regular-type blank sections during the coal extraction period are compared and analyzed by the underground–ground integrated microseismic monitoring data such as microseismic energy level and frequency as well as the online stress monitoring data. The results show that leaving the triangular-type blank sections can result in reduced construction costs without compromising the effectiveness of rockburst prevention and control. Additionally, the performance of rockburst prevention and control in regular-type blank sections is notably superior to that observed in other working faces without hydraulic fracturing. However, when compared to fracturing sections, the efficacy of rockburst prevention and control in regular-type blank sections remains relatively inferior. Therefore, during the design of fracturing boreholes, it is imperative to strive for maximum coverage of regular-type blank sections. The research findings of this paper comprehensively summarize two prevalent types of blank sections encountered in directional long borehole staged hydraulic fracturing projects. A rigorous comparative analysis is undertaken to evaluate the rockburst prevention and control effects between fractured sections and blank sections. This comparative evaluation serves as a valuable reference for the optimal design of fracturing boreholes, ensuring a balance between achieving effective rockburst prevention and control measures and minimizing economic costs. Full article

Rockburst is a common dynamic geological disaster in underground mining and tunneling engineering, characterized by randomness, abruptness, and impact. Short-term evaluation of rockburst potential plays an outsize role in ensuring the safety of workers, equipment, and projects. As is well known, microseismic monitoring serves as a reliable short-term early-warning technique for rockburst. However, the large amount of microseismic data brings many challenges to traditional manual analysis, such as the timeliness of data processing and the accuracy of rockburst prediction. To this end, this study integrates artificial intelligence with microseismic monitoring. On the basis of a comprehensive consideration of class imbalance and multicollinearity, an innovative modeling framework that combines local outlier factor-guided synthetic minority oversampling and an extremely randomized forest with C5.0 decision trees is proposed for the short-term evaluation of rockburst potential. To determine the optimal hyperparameters, the whale optimization algorithm is embedded. To prove the efficacy of the model, a total of 93 rockburst cases are collected from various engineering projects. The results show that the proposed approach achieves an accuracy of 90.91% and a macro F₁-score of 0.9141. Additionally, the local F₁-scores on low-intensity and high-intensity rockburst are 0.9600 and 0.9474, respectively. Finally, the advantages of the proposed approach are further validated through an extended comparative analysis. The insights derived from this research provide a reference for microseismic data-based short-term rockburst prediction when faced with class imbalance and multicollinearity. Full article

Rockburst, one of the leading types of disaster in mining and rock engineering causing serious injuries and the loss of property, frequently occurs, involving various features and complex evolutionary mechanisms. Compared to rockbursts occurring at mining faces, those occurring in main roadways cause more serious problems for mine production. This paper first analyzes the characteristics of rockbursts in main roadways using two case studies involving the Gaojiapu and Cuimu coal mines. The causes of rockbursts in main roadways were studied using microseismic monitoring, energy density cloud maps, and seismic velocity tomography. During the mining of the 22306 working face in the Cuimu coal mine, targeted measures, such as deep-hole blasting of the roof strata and deep-hole blasting of the coal seam, were implemented to prevent rockbursts in the main roadways. The effectiveness of these measures was verified through long-term analysis of tremor activities. The study found that the influence of mining at two working faces on both sides of main roadways was significantly greater than that from a single-sided working face. The intensity of the tremor activities occurring near the main roadways was correlated with the distance from the working face to the main roadways. The closer the working face was to the main roadways, the stronger the tremor activities were near the main roadways. According to the distribution range of the tremors, the influence area of working face mining exceeded 800 m, with tremors distributed linearly along the main roadways. Even five months after the completion of working face mining, there were still a large number of tremors near the main roadways, which gradually disappeared after another five months. Mining activities were the main reason for the occurrence of main roadway rockbursts and the stress concentration within the main roadways themselves was another reason for the occurrence of rockbursts. The influence of working face mining could be reduced by deep-hole blasting of roof strata and the stress concentration within main roadways themselves could be reduced by large-diameter drilling. Those joint preventive measures effectively prevented the occurrence of rockbursts in main roadways. This study is of important theoretical and practical significance for further studies of rockburst mechanisms and prevention in regard to main roadways in coal mines, and the findings are significant in terms of the enhancement of safety in coal mines. Full article

As a unique geological hazard in a high-geo-stress environment, rockburst happens with strong suddenness, randomness, and destructiveness, but the mechanism of its occurrence in a deep-buried tunnel in a high-geo-stress environment needs further study. Based on the analyses of the stress field of the rockburst section of Ping’an Tunnel, which is over-long and deep-buried, the occurrence mechanism of rockburst is figured out. Furtherly, the intensity and location of rockburst are predicted by using the rockburst criterion. Results show that there exists large compressive stress at the side wall of the tunnel, which is the main cause of rockburst. Under a high-geo-stress condition, due to the existence of unfavorable factors like structural planes, high intensity rockburst is likely to happen in the middle of the tunnel face after the sudden release of original rock stress. Arranging stress-releasing holes in the rockburst section can effectively reduce the possibility of rockburst by releasing the original rock stress in advance. The research results can deepen the understanding of the mechanism of rockburst in a high-geo-stress environment and provide scientific basis for the prevention and control of rockburst in similar engineering projects. Full article

Rockburst is an extremely hazardous geological disaster. In order to accurately predict the hazardous degree of rockbursts, this paper proposes eight new classification models for predicting the intensity level of rockbursts based on intelligent optimisation algorithms and deep learning techniques and collects 287 sets of real rockburst data to form a sample database, in which six quantitative indicators are selected as feature parameters. In order to validate the effectiveness of the constructed eight machine learning prediction models, the study selected Accuracy, Precision, Recall and F1 Score to evaluate the prediction performance of each model. The results show that the NGO-CNN-BiGRU-Attention model has the best prediction performance, with an accuracy of 0.98. Subsequently, engineering validation of the model is carried out using eight sets of real rockburst data from Daxiangling Tunnel, and the results show that the model has a strong generalisation ability and can satisfy the relevant engineering applications. In addition, this paper also uses SHAP technology to quantify the impact of different factors on the rockburst intensity level and found that the elastic strain energy index and stress ratio have the greatest impact on the rockburst intensity level. Full article

To investigate the influence of water content on the rockburst phenomena in tunnels with horizontal joints, experiments were conducted on simulated rock specimens exhibiting five distinct levels of water absorption. Real-time monitoring of the entire blasting process was facilitated through a high-speed camera system, while the microscopic structure of the rockburst debris was analyzed using scanning electron microscopy (SEM) and a particle size analyzer. The experimental findings revealed that under varying degrees of water absorption, the specimens experienced three stages: debris ejection; rockburst; and debris spalling. As water content increased gradually, the intensity of rockburst in the specimens was mitigated. This was substantiated by a decline in peak stress intensity, a decrease in elastic modulus, delayed manifestation of pre-peak stress drop, enhanced amplitude, diminished elastic potential energy, and augmented dissipation energy, resulting in an expanded angle of rockburst debris ejection. With increasing water content, the bond strength between micro-particles was attenuated, resulting in the disintegration of the bonding material. Deformation failure was defined by the expansion of minuscule pores, gradual propagation of micro-cracks, augmentation of fluffy fine particles, exacerbation of structural surface damage akin to a honeycomb structure, diminishment of particle diameter, and a notable increase in quantity. Furthermore, the augmentation of secondary cracks and shear cracks, coupled with the enlargement of spalling areas, signified the escalation of deformation failure. Simultaneously, the total mass of rockburst debris gradually diminished, accompanied by a corresponding decrease in the proportion of micro and fine particles within the debris. Full article

Upon reaching deeper levels of extraction, dynamic hazards such as rockburst become more pronounced, with the high energy storage characteristics of rock masses in high-stress environments being the fundamental factor behind rockburst disasters. Additionally, deep-seated mineral extraction commonly involves drilling and blasting methods, where the vibrational energy generated by mining explosions combines with the elastic energy of rock masses, leading to a sudden growth in the risk and intensity of rockburst disasters. This paper, with deep mining at Sanshandao Gold Mine as the focal point, systematically investigates the impact of blasting vibrations on rockburst disasters in deep mines. Initially, based on extensive data on measured geostress considering the tri-arch cross-section form of deep tunnels, the elastic energy storage of the surrounding rocks in deep tunnels was calculated. The results indicate that the maximum energy storage of the surrounding rocks occurs at the bottom of the tunnel, with the peak accumulation position located at a distance of five times the tunnel radius. On this basis, the Map3D numerical simulation analysis was adopted to systematically capture the accumulation behavior and distribution characteristics of disturbance energy. Subsequently, by conducting the dynamic impact experiments with an improved Split Hopkinson pressure bar (SHPB) and monitoring vibration signals at various locations, the paper provides insights into the propagation patterns of impact energy in a long sample (400 mm in length and 50 mm in diameter). Analysis of the scattering behavior of vibrational energy reveals that the combined portion of blasting vibration energy constitutes 60% of the total vibrational energy. Finally, a rockburst disaster evaluation model based on energy accumulations was proposed to analyze the rockburst tendencies around deep tunnels. The results indicated that the disaster-driven energy increased by 19.9% and 12.2% at different places on the roadway. Also, the probability and intensity of a rockburst would be raised. Full article

Rockbursts are hazardous phenomena of sudden and violent rock failure in deep underground excavations under high geostress conditions, which poses a serious threat to geotechnical engineering. The occurrence of rockbursts is influenced by a combination of factors. Therefore, it is necessary to find an efficient method to assess rockburst grades. In this paper, we propose a novel method that enhances the VIKOR method using a novel combination of weight and generalized weighted Mahalanobis distance. The combination weights of the evaluation indicators were calculated using game theory by combining subjective experience and objective data statistical characteristics. By introducing the generalized weighted Mahalanobis distance, the VIKOR method is improved to address the issues of inconsistent dimensions, different importance, and inconsistent correlation among indicators. The proposed method can deal with the complexity of the impact factors of rockburst evaluation and classify the rockburst intensity level. The results show that the accuracy of the improved VIKOR method with the distance formula is higher than that of the unimproved VIKOR method; the evaluation accuracy of the improved VIKOR method with the generalized weighted Mahalanobis distance is 91.67%, which outperforms the improved VIKOR methods with the Euclidean and Canberra distances. This assessment method can be easily implemented and does not depend on the discussion of the rockburst occurrence mechanism, making it widely applicable for engineering rockburst evaluation. Full article

Rigid structural planes encountered during construction have an obvious influence on rockburst intensity and occurrence mechanism. The high-intensity rockburst induced by the structural plane poses a great threat to the safety construction of the tunnel. A novel 3D discrete element numerical analysis method for rockburst is proposed with the help of the bonded block model and multi-parameter rockburst energy index. According to this method, the influence of multivariate information characteristics such as stress, energy, fracture, and rockburst proneness index on the surrounding rock during the strain-structural plane slip rockburst in deep tunnels is systematically investigated. The results are drawn as follows: The results show that from the analysis of multivariate information characteristics of strain-structural plane slip rockburst, the rock between the plane and tunnel is a typical rockburst risk area. The dip angle, length, and relative distance of the structural plane have a significant influence on the multivariate catastrophe information characteristics of the surrounding rock: As the dip angle increases, the fracture propagation range within the risk rock expands, but the rockburst intensity and the occurrence range gradually decrease; as the length increases, the fracture propagation range, rockburst intensity and occurrence range within the risk rock increase slightly; as the relative distance increases, the fracture propagation range and rockburst intensity gradually weaken, and the occurrence range of rockburst first increases and then decreases. Using the “11.28” strain-structural plane slip rockburst case as a basis, engineering validation research was conducted. The simulation results are found to be essentially consistent with the rockburst condition on the field, validating the rationality and applicability of the novel rockburst analysis method proposed in this paper. Full article