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Predictive Models for Permeability of Cracked Rock Masses Based on Support Vector Machine Techniques
Authors:Ma  Guotao  Chao  Zhiming  He  Kun
Institution:1.School of Engineering, The University of Warwick, Coventry, CV4 7AL, UK
;2.Research and Development Department, DP Consultation Company, Chengdu, 610000, China
;
Abstract:

In this study, a database developed from existing literature about permeability of cracked rock was established. The performance of Support Vector Machine (SVM) combined with optimisation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimisation Algorithm (PSO) in predicting the permeability of cracked rock masses (CRM) is evaluated. Also, the sensitivity analysis of the influence factors to the permeability of CRM is conducted. The results indicate that the hybrid GA–SVM and hybrid PSO–SVM models can accurately predict the permeability of CRM in terms of the statistical performance criteria: Coefficient of Determination R2, Regression Coefficient R and Mean Residual Error (MSE); Additionally, optimisation algorithms: PSO and GA can improve significantly the predictive performance of the SVM model. Based on the sensitivity analysis, crack angle is the most important factor to change the permeability of CRM, followed by confining pressure.

Keywords:
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