scholarly journals Shear Strength and Propagation of Seismic Wave during Rock Joint Shear Test under Constant Pressure and Constant Dilatancy.

2002 ◽  
pp. 95-105
Author(s):  
Chikaosa TANIMOTO ◽  
Kenjiro MIYATA ◽  
Satoru KAWASAKI ◽  
Yoshinori KITAMURA ◽  
Junya SUZUKI
Keyword(s):  
Shear Strength ◽  
Seismic Wave ◽  
Shear Test ◽  
Constant Pressure ◽  
Rock Joint ◽  
Joint Shear

Nonlinear models based on enhanced Kriging interpolation for prediction of rock joint shear strength

2020 ◽  
Author(s):  
Mahdi Hasanipanah ◽  
Debiao Meng ◽  
Behrooz Keshtegar ◽  
Nguyen-Thoi Trung ◽  
Duc-Kien Thai
Keyword(s):  
Shear Strength ◽  
Nonlinear Models ◽  
Rock Joint ◽  
Kriging Interpolation ◽  
Joint Shear Strength ◽  
Joint Shear

scholarly journals Nonlinearity of the Rock Joint Shear Strength

2015 ◽  
Vol 47 (1) ◽  
pp. 205-212 ◽  
Author(s):  
Y. F. Wei ◽  
W. X. Fu ◽  
D. X. Nie
Keyword(s):  
Shear Strength ◽  
Rock Joint ◽  
Joint Shear Strength ◽  
Joint Shear

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

Lithosphere ◽  
2021 ◽  
Vol 2021 (Special 4) ◽  
Author(s):  
Jiandong Huang ◽  
Jia Zhang ◽  
Yuan Gao
Keyword(s):  
Machine Learning ◽  
Shear Strength ◽  
Support Vector Regression ◽  
Firefly Algorithm ◽  
Rock Joint ◽  
Rock Joints ◽  
Support Vector ◽  
Joint Shear Strength ◽  
Joint Shear ◽  
Combined Algorithm

Abstract 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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