Utilizing artificial neural networks to anticipate early-age thermal parameters in concrete piers

Abstract

Recently, researches have been used Artificial Neural Network (ANN) to predict the early-age thermal cracking of rectangle piers. But ANN has not resulted for different types of concrete piers. This article presents an evaluation of the early-age thermal characteristics of mass concrete piers with four distinct cross-sectional shapes. A finite element (FE) model was employed to estimate the maximum temperature, thermal stress, and cracking potential of the concrete pier at its early age. To investigate the impact of various pier geometries on the thermal cracking potential, different pier geometries were considered. In this study, an ANN model was utilized to predict the maximum temperature and decrease the risk of cracking in mass concrete piers at early age. The database of thermal mass concrete piers used in this study comprises 128 results obtained from the FE model. The results of the analysis indicate that the ANN model can predict early-age thermal parameters, and cracking risk in early-age concrete piers with good accuracy and help to the designer to choose the appropriate size in minimizing cracks on the pier concrete.