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

Askaripour, Mahdi; Saeidi, Ali; Rouleau, Alain; Mercier-Langevin, P

doi:10.1016/j.undsp.2021.11.008

Cited by 79 publications

(24 citation statements)

References 79 publications

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“…As shown in Figure 8(b), the maximum shear stress of the surrounding rock on the roof was 6.35 MPa, the average uniaxial compressive strength of the roof specimen was 58.96 MPa, and the ratio of the maximum shear stress to the uniaxial compressive strength was 0.11 (<0.30). The categorisation of rock burst strength based on the maximum shear stress is presented in Table 3 (Askaripour et al. , 2022).…”

Section: Resultsmentioning

confidence: 99%

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Numerical analysis for tunnelling-induced stress and plastic evolution causing instability of multilayered surrounding rock by varying three-dimensional in situ stresses

Wang,

Feng,

et al. 2023

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PurposeThe induction of geological disasters is predominantly influenced by the dynamic evolution of the stress and plastic zones of the multilayer rock formations surrounding deep-rock roadways, and the behaviours and mechanisms of high in situ stress are key scientific issues related to deep-resource exploitation. The stress environment of deep resources is more complex owing to the influence of several geological factors, such as tectonic movements and landforms. Therefore, in practical engineering, the in situ stress field is in a complex anisotropic three-dimensional state, which may change the deformation and failure law of the surrounding rock. The purpose of this study is to investigate the tunnelling-induced stress and plastic evolution causing instability of multilayered surrounding rock by varying three-dimensional in situ stresses.Design/methodology/approachBased on data from the Yangquan Coal Mine, China, a finite difference model was established, and the elastic-plastic constitutive model and element deletion technology designed in the study were analysed. Gradual tunnelling along the roof and floor of the coal seam was used in the model, which predicted the impact tendency, and compared the results with the impact tendency report to verify the validity of the model. The evolutions of the stress field and plastic zone of the coal roadway in different stress fields were studied by modifying the maximum horizontal in situ stress, minimum horizontal in situ stress and lateral pressure coefficient.FindingsThe results shown that the in situ stress influenced the stress distribution and plastic zone of the surrounding rock. With an increase in the minimum horizontal in situ stress, the vertical in situ stress release area of the roof surrounding rock slowly decreased; the area of vertical in situ stress concentration area of the deep surrounding rock on roadway sides decreased, increased and decreased by turn; the area of roof now-shear failure area first increased and then decreased. With an increase in the lateral pressure coefficient, the area of the horizontal in situ stress release area of the surrounding rock increased; the area of vertical in situ stress release area of the roof and floor surrounding rock first decreased and then increased; the area of deep stress concentration area of roadway sides decreased; and the plastic area of the surrounding rock and the area of now-shear failure first decreased and then increased.Originality/valueThe results obtained in this study are based on actual cases and reveal the evolution law of the disturbing stress and plastic zone of multilayer surrounding rock caused by three-dimensional in situ stress during the excavation of deep rock roadways, which can provide a practical reference for the extraction of deep resources.

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Section: Resultsmentioning

confidence: 99%

“…Lithology (Askaripour et al, 2022). Based on the maximum shear stress, the predicted results of the rock burst strength on the floor and roof of the coal seam were no rock bursts.…”

Section: Layers Numbermentioning

confidence: 99%

Numerical analysis for tunnelling-induced stress and plastic evolution causing instability of multilayered surrounding rock by varying three-dimensional in situ stresses

Wang,

Feng,

et al. 2023

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“…The mechanical and physical characteristics of rocks are important to engineering applications. Such properties can be influenced by the rock's petrographic characteristics, including its composition and texture, which result from the environment of formation (e.g., sedimentation, diagenesis, metamorphism, and weathering) [187]. The current review indicates that mineral composition is the most dominant feature in rock strength.…”

Section: Discussionmentioning

confidence: 98%

A Review of Relationship between Texture Characteristic and Mechanical Properties of Rock

et al. 2022

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The textural characteristics of rocks influence their petrophysical and mechanical properties. Such parameters largely control rock mass stability. The ability to evaluate both immediate and long-term rock behaviors based on the interaction between various parameters of rock texture, petrophysical and mechanical properties is therefore crucial to many geoengineering facilities. However, due to the common lack of high-quality core samples for geomechanics and rock texture laboratory tests, single and multivariable regression analyses are conducted between mechanical properties and textural characteristics based on experimental test data. This study presents a review of how rock texture characteristics influence the geomechanical properties of a rock, and summarizes the regression equations between two aspects. More specifically, a review of the available literature on the effects of mineralogy, grain size, grain shape, packing density, foliation index, porosity, degree of weathering, and other rock physical characteristics on geomechanics is presented. Similarly, a review of the literature discussing the failure criteria of anisotropic rocks, both continuous and discontinuous, is also presented. These reviews are accompanied by a comparison of the fundamentals of these methods, describing their equations and discussing their advantages and disadvantages. This exercise has the objective of providing better guidelines on how to use these criteria, allowing for safer underground excavations via an improved understanding of how rock texture parameters affects the mechanical behavior of rocks.

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“…7 However, there is a noticeable lack of research on the prediction of displacement effects based on deep learning algorithms in the context of gas injection displacement technology as applied to coal seams. In the field of safety management research, several techniques have been employed for risk assessment, including empirical, 8,9 analytical, 10,11 numerical, [12][13][14] experimental, 15,16 intelligent, [17][18][19] and expert system 20 approaches. Every method has its benefits and drawbacks.…”

Section: Introductionmentioning

confidence: 99%

A novel intelligent risk prediction model for the effectiveness of CO₂/N₂–ECGD technology

Wang,

et al. 2024

Energy Science & Engineering

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To solve the problems of high sampling requirements and low predictive accuracy resulting from the complexity, uniqueness, and randomness of predicting the risk of the CO2 and N2 injection to enhance coal seam gas drainage (CO2/N2–ECGD) technology. The principal component analysis (PCA) method to reduce the dimensionality of the factor data that contribute to the effect risk of the technology was adopted. And the particle swarm optimization (PSO) method was implemented to search for optimal hyperparameters in support vector machine (SVM) by particle search, as a solution to the traditional SVM hyperparameters optimization problem. A novel risk prediction model using machine learning algorithms for gas injection displacement technology was constructed. The prediction results were tested and compared with those of backpropagation (BP), Random Forest (RF), and Decision Tree (DT) models using data from 29 gas injection displacement field projects in China. The results demonstrated that the SVM model had greater accuracy in prediction than the other three models. Additionally, after PSO optimization and dimensionality reduction, the PCA–PSO–SVM model reached 100% prediction accuracy, while requiring less modeling and operation time. The study provided a reliable and reasonable model for predicting technical effects, along with a theoretical basis for risk management and prevention. First, the technology's influencing indicators were analyzed by examining its mechanisms. Second, we utilized the PCA method to reduce the dimensionality of the factor data that contribute to the risk of the technology's effects. Third, we implemented the PSO method to search for optimal hyperparameters in the SVM through particle search, as a solution to the traditional SVM hyperparameters optimization problem. Finally, the prediction results were tested and compared with those of BP, RF, and DT models using data from 29 gas injection displacement field projects in China. The SVM model was found to have greater accuracy in prediction than the other three models. After PSO optimization and dimensionality reduction, the PCA–PSO–SVM model achieved 100% prediction accuracy while requiring less modeling and operation time. The study presents a valid and reasonable model for predicting technical effects and a theoretical basis for risk management and prevention.

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

Cited by 79 publications

References 79 publications

Numerical analysis for tunnelling-induced stress and plastic evolution causing instability of multilayered surrounding rock by varying three-dimensional in situ stresses

Numerical analysis for tunnelling-induced stress and plastic evolution causing instability of multilayered surrounding rock by varying three-dimensional in situ stresses

A Review of Relationship between Texture Characteristic and Mechanical Properties of Rock

A novel intelligent risk prediction model for the effectiveness of CO₂/N₂–ECGD technology

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

Cited by 79 publications

References 79 publications

Numerical analysis for tunnelling-induced stress and plastic evolution causing instability of multilayered surrounding rock by varying three-dimensional in situ stresses

Numerical analysis for tunnelling-induced stress and plastic evolution causing instability of multilayered surrounding rock by varying three-dimensional in situ stresses

A Review of Relationship between Texture Characteristic and Mechanical Properties of Rock

A novel intelligent risk prediction model for the effectiveness of CO2/N2–ECGD technology

Contact Info

Product

Resources

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A novel intelligent risk prediction model for the effectiveness of CO₂/N₂–ECGD technology