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http://dx.doi.org/10.12989/cac.2012.9.3.215

Utilization of support vector machine for prediction of fracture parameters of concrete  

Samui, Pijush (Centre for Disaster Mitigation and Management, VIT University)
Kim, Dookie (Department of Civil Engineering, Kunsan National University)
Publication Information
Computers and Concrete / v.9, no.3, 2012 , pp. 215-226 More about this Journal
Abstract
This article employs Support Vector Machine (SVM) for determination of fracture parameters critical stress intensity factor ($K^s_{Ic}$) and the critical crack tip opening displacement ($CTOD_c$) of concrete. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ${\varepsilon}$-insensitive loss function has been adopted. The results are compared with a widely used Artificial Neural Network (ANN) model. Equations have been also developed for prediction of $K^s_{Ic}$ and $CTOD_c$. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The results of this study show that the developed SVM is a robust model for determination of $K^s_{Ic}$ and $CTOD_c$ of concrete.
Keywords
concrete; fracture mechanics; support vector machine; sensitivity analysis; artificial neural network; two-parameter model;
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