Title: Artificial Neural Network Model for Predicting Compressive Strength of High Strength Concrete after Burning

Abstract:-- In this investigation, the effects of elevated temperatures of 300, 550, 750°C for 1.0 hour at different ages of 28 , 56 and 90 days on the main mechanical properties of high strength concrete are studied. After burning, the concrete specimens were quenched in a water tank to provide the maximum shock due to sudden cooling. This study presents the effort in applying neural network-based system identification techniques to predict the mechanical properties of high strength. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets from experimental. The parametric study shows that temperature is the most significant factor affecting the output of the model. The results showed that artificial neural networks (ANN) has strong potential as a feasible tool for predicting compressive strength of concrete after exposure to fire flame.