Engineering Properties and Prediction of Strength of High Performance Fibre Reinforced Concrete using Artificial Neural Networks

This paper presents the experimental and numerical studies on high performance fiber concrete (HPFRC) with water-cementitious materials (w/cm) ratios of 0.4- 0.3, steel fiber volume fraction (Vf) varying from 0- 1.5%, polypropylene fiber volume fraction varying from 0- 1% and silica fume replacement at 10% and 15%. Experimental results showed high improvements in 28 day cylinder compressive strength and flex-ural strength of steel fiber reinforced concrete at fiber volume fraction of 1.5%; for polypropylene (PP) FRC improvement in compressive and flexural strengths are marginal and moderate, respectively. Statistical models developed for compressive strength ratios and flexural strength ratios of HPSFRC indicate the prediction ca-pabilities of the models. Due to the complex mix proportions of HPSFRC and the non-linear relationship be-tween the concrete mix proportions and properties, research on HPSFRC has been empirical and no models with reliable predictive capabilities for its behavior have been developed. Based on the large data collected for HPSFRC mixes, a trained artificial neural network (ANN) model which adopts a back propagation algorithm to predict 28-day compressive strength of HPSFRC mixes was employed. This paper describes the comparison of the experimental results obtained for various mixes. Multiple linear regression (MLR) model with R2 = 0.78 was also developed for the prediction of compressive strength of HPSFRC mixes. On validation of the data sets by NNs, the error range is within 2% of the actual values. ANN models give the significant degree of ac-curacy compared to MLR model, and can be easily used to estimate the strength of concrete mixes.