Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization

Abstract

Cement concrete is the most commonly used material today for constructing residential or commercial buildings, industrial parks, or particular components such as tunnel slabs where there is a high risk of fire. This structure requires concrete to be subjected to high temperatures generated by fires. However, concrete under the influence of high temperature has very complex behavior states with deformations, physical and chemical changes as the temperature rises dramatically. In this study, an artificial neural network-based Bayesian regularization (ANN) model is proposed to predict the compressive strength of concrete. The database in this study includes 208 experimental results synthesized from laboratory experiments with 9 input variables related to temperature change and design material composition. The performance of the ANN model was evaluated using K-fold cross-validation and statistical criteria, including mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results show that the proposed ANN model is a reasonable, highly accurate, and useful prediction tool for saving time and minimizing costly experiments.