Abstract
AbstractTo 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.
Highlights
It often appeared in mining engineering construction the issues to deal with the rock joint, which is heterogeneous and anisotropic and often contains a large number of faults, bedding, structural cracks, muddy interlayer, and other geological structural planes of different genetic types [1,2,3,4,5,6,7,8,9,10]
It is evident that the firefly algorithm (FA) algorithm can adjust the hyperparameters effectively and accurately, obtaining the optimized Support vector regression (SVR) model to complete the prediction of rock joint shear strength
The predicted and measured τp, indicating that the SVR-FA model has a high prediction accuracy and the algorithm is suitable for the dataset of rock joint shear strength
Summary
It often appeared in mining engineering construction the issues to deal with the rock joint, which is heterogeneous and anisotropic and often contains a large number of faults, bedding, structural cracks, muddy interlayer, and other geological structural planes of different genetic types [1,2,3,4,5,6,7,8,9,10]. The strength, deformation, and stability of the rock mass are mainly controlled by the joint surface [11,12,13,14]. Large-scale rock mass-engineering disasters are mainly caused by the expansion, evolution, and penetration of existing joints [4, 12, 21]. To propose an effective and reasonable excavation plan for rock joints and support design parameters to control the overall stability of the surrounding rock mass and predict and prevent engineering disasters, it is necessary to accurately evaluate the rock joint shear strength (τp) [2, 5, 12, 13]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
