Open Access

ARTICLE

ANN Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

Yuvaraj P¹, A Ramachra Murthy², Nagesh R Iyer³, S.K. Sekar⁴, Pijush Samui⁵

1 Bridges Department, L&T Ramboll Consulting Engineers Ltd, Guindy, Chennai, India. E-mail:yuvarajprakash@gmail.com
2 Scientist, CSIR-Structural Engineering Research Centre, Taramani, Chennai, India. E-mail:murthyarc@serc.res.in
3 Director, CSIR-Structural Engineering Research Centre, Taramani, Chennai, India. E-mail:nriyer@serc.res.in
4 Director, CDMM, Vellore Institute of Technology, VIT University, Vellore, India. E-mail:sksekar@vit.ac.in
5 Professor, CDMM, Vellore Institute of Technology, VIT University, Vellore, India. E-mail: pijush-samui@vit.ac.in

Computers, Materials & Continua 2014, 41(3), 193-214. https://doi.org/10.3970/cmc.2014.041.193

Abstract

This paper presents fracture mechanics based Artificial Neural Network (ANN) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (Gf), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). Failure load of the beam (Pmax) is also predicated by using ANN model. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. Back-propagation training technique has been employed for updating the weights of each layer based on the error in the network output. Levenberg- Marquardt algorithm has been used for feed-forward back-propagation. Four ANN models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load. ANN has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, Gf, failure load, KIc and CTODc are in good agreement with those of the experimental values.

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