Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete beams

This paper examines the applicability of support vector machine (SVM) based regression to predict fracture characteristics and failure load (Pmax) of high strength and ultra high strength concrete beams. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described briefly. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. Support Vector Regression (SVR) is the extension of SVMs to solve regression and prediction problems. The main characteristics of SVR includes minimizing the observed training error, attempts to minimize the generalized error bound so as to achieve generalized performance. Four Support Vector Regression (SVR) models have been developed using MATLAB software for training and prediction of fracture characteristics. It is observed that the predicted values from the SVR models are in good agreement with those of the experimental values.

► Evaluation of mechanical properties of high and ultra high strength concrete. ► Experimental evaluation of fracture characteristics. ► Development of support vector machine based regression models. ► Validation studies with the corresponding experimental observations.