Shear velocity-based uncertainty quantification for rock joint shear strength

Tiwari, Gaurav; Latha, G. Madhavi

doi:10.1007/s10064-019-01496-0

Cited by 18 publications

(4 citation statements)

References 20 publications

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“…However, the joint shear strength estimated under low shear rates in the laboratory cannot be applied to field conditions. To study the impact of high shear rates on shear performance, Tiwari 55 developed a probabilistic method to estimate the in-situ shear strength of joints under high displacement rate conditions. That study found that the shear strength of structural plane decreases with increasing shear rate, and this dependence is more significant under low-density rock and high confining stress conditions.…”

Section: Relative Workmentioning

confidence: 99%

Shear damage mechanisms of jointed rock mass: a macroscopic and mesoscopic study

Wang,

Liu,

Jiang

et al. 2024

Sci Rep

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The joints are existing throughout the underground rock mass. It is of great significance to investigate the shear performance of the rock mass to maintain the stability of the underground structure. In this study, we conducted orthogonal tests to determine the proportion of rock-like materials, and used JRC curves to make specimen molds and then prepare the specimens. We conducted straight shear tests and uniaxial compression tests to determine the various mechanical parameters of the rock-like materials. Next, we carried out the compression and shear tests to investigate the shear characteristics of the specimens, and study the damage pattern and shear strength of the jointed rock mass under different confining pressures and roughness levels. The mesoscopic displacements in the shear process of joints were analyzed by using ABAQUS. The test results show that the effect of the confining pressure on the shear strength of the joint plane is relatively obvious, and a larger confining pressure indicates a larger shear strength. The effects of different joint plane roughness and shear rated on the shear characteristics of the joint plane are also significant. The mesoscopic displacement difference inside the joint plane with higher roughness is relatively large, and the stress concentration phenomenon is obvious and lasts longer, which leads to the faster destruction of the specimen with higher roughness and the higher destruction degree. Therefore, we suggest that the priority should be given to the reinforcement of jointed rock mass with high roughness during the construction to prevent sudden destabilization and failure.

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Section: Relative Workmentioning

confidence: 99%

Shear damage mechanisms of jointed rock mass: a macroscopic and mesoscopic study

Wang,

Liu,

Jiang

et al. 2024

Sci Rep

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“…e mining waste includes the stripping and waste rock (including coal gangue) produced in the process of mining, as well as the tailings discarded in the process of mineral dressing [1][2][3][4][5][6]. A large amount of mining solid waste as well as the complicated treatment has become one of the issues that should be addressed for environmental protection [4,[7][8][9][10][11][12]. e massive accumulation of mining waste can cause pollution of the land, causing disasters such as landslides and mudslides.…”

Section: Introductionmentioning

confidence: 99%

“…e debris and tailings formed by the weathering of waste rock are either washed by water into the water body, dissolved and penetrated into the groundwater, or blown into the atmosphere by the wind, polluting the environment with water and gas as the medium [13][14][15][16]. ese mining waste materials not only take up a lot of land but also directly pollute the environment and threaten the safety of people's lives, since some of these wastes contain highly toxic elements, such as arsenic and cadmium, and radioactive elements, which are harmful to human health [1,2,4,[7][8][9]. Tailings have the characteristics of fine particles, small weight, large surface area, being easy to run away in contact with water, and being easy to fly when exposed to wind.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Compressive Strength of Cement‐Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models

Liu

Zhang

et al. 2021

Advances in Civil Engineering

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To estimate the compressive strength of cement-based materials with mining waste, the dataset based on a series of experimental studies was constructed. The support vector machine (SVM), decision tree (DT), and random forest (RF) models were developed and compared. The beetle antennae search (BAS) algorithm was employed to tune the hyperparameters of the developed machine learning models. The predictive performances of the three models were compared by the evaluation of the values of correlation coefficient (R) and root mean square error (RMSE). The results showed that the BAS algorithm can effectively tune these artificial intelligence models. The SVM model can obtain the minimum RMSE, while the BAS algorithm is inefficient in DT and RF models. The SVM, DT, and RF models can be used to predict the compressive strength of cement-based materials using solid mining waste as aggregate effectively and accurately, with high R values and lower RMSE values. The RF algorithm can obtain the highest value of R and the lowest value of RMSE, demonstrating the highest accuracy. The solid mining waste to cement ratio is the most important variable to affect the compressive strength. Curing time was also an important parameter in the compressive strength of cemented materials, followed by the water-solid ratio of mining waste and fine sand ratio.

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“…The strength, deformation, and stability of the rock mass are mainly controlled by the joint surface [11][12][13][14]. As a natural defect formed in the long geological process, joints exist in the rock mass with complex geometric shapes, scales, and filling forms, which seriously affect and restrict the mechanical behavior and deformation, and failure characteristics of the rock mass [15][16][17][18][19][20]. Large-scale rock mass-engineering disasters are mainly caused by the expansion, evolution, and penetration of existing joints [4,12,21].…”

Section: Introductionmentioning

confidence: 99%

Intelligently Predict the Rock Joint Shear Strength Using the Support Vector Regression and Firefly Algorithm

Huang¹,

Gao²,

Liu³

2021

Lithosphere

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To propose an effective and reasonable excavation plan for rock joints to control the overall stability of the surrounding rock mass and predict and prevent engineering disasters, this study is aimed at predicting the rock joint shear strength using the combined algorithm by the support vector regression (SVR) and firefly algorithm (FA). The dataset of rock joint shear strength collected was employed as the output of the prediction, using the joint roughness coefficient (JRC), uniaxial compressive strength (σc), normal stress (σn), and basic friction angle (φb) as the input for the machine learning. Based on the database of rock joint shear strength, the training subset and test subset for machine learning processes are developed to realize the prediction and evaluation processes. The results showed that the FA algorithm can adjust the hyperparameters effectively and accurately, obtaining the optimized SVR model to complete the prediction of rock joint shear strength. For the testing results, the developed model was able to obtain values of 0.9825 and 0.2334 for the coefficient of determination and root-mean-square error, showing the good applicability of the SVR-FA model to establish the nonlinear relationship between the input variables and the rock joint shear strength. Results of the importance scores showed that σn is the most important factor that affects the rock joint shear strength while σc has the least significant effect. As a factor influencing the shear stiffness from the perspective of physical appearance, the change of the JRC value has a significant impact on the rock joint shear strength.

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Shear velocity-based uncertainty quantification for rock joint shear strength

Cited by 18 publications

References 20 publications

Shear damage mechanisms of jointed rock mass: a macroscopic and mesoscopic study

Shear damage mechanisms of jointed rock mass: a macroscopic and mesoscopic study

Estimating the Compressive Strength of Cement‐Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models

Intelligently Predict the Rock Joint Shear Strength Using the Support Vector Regression and Firefly Algorithm

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