Title:
Model of Least Square Support Vector Machine (LSSVM) for Prediction of Fracture Parameters of Concrete
Author(s):
Kulkrni Kallyan S., Dookie Kim, S. K. Sekar, and Pijush Samui
Publication:
IJCSM
Volume:
5
Issue:
1
Appears on pages(s):
29-33
Keywords:
least square support vector machine (LSSVM); fracture mechanics; artificial neural network (ANN); support vector machine (SVM)
DOI:
Date:
6/30/2011
Abstract:
This article employs Least Square Support Vector Machine (LSSVM) for determination of fracture parameters of concrete: critical stress intensity factor ( ) and the critical crack tip opening displacement ( ). LSSVM that is firmly based on the theory of statistical learning theory uses regression technique. The results are compared with a widely used Artificial Neural Network (ANN) Models of LSSVM have been developed for prediction of and , and then a sensitivity analysis has been performed to investigate the importance of the input parameters. Equations have been also developed for determination of and . The developed LSSVM also gives error bar. The results show that the developed model of LSSVM is very predictable in order to determine fracture parameters of concrete.