Evaluation of fracture toughness properties of polymer concrete composite using deep learning approach.

Using artificial intelligence‐based methods in predicting material properties, in addition to high accuracy, saves time and money. This paper models and predicts the fracture toughness properties of polymer concrete (PC) composites using the deep learning method. After preparing a database consisting of 209 experimental data from 19 relevant studies, the effect of seven important variables on critical stress intensity factor (KIc) and crack tip opening displacement (CTOD) is considered. Then, the deep neural network (DNN) model is developed and trained using the prepared database. The accuracy of the DNN model is examined by implementing four statistical criteria, MSE, R2, RMSE, and MAE. Finally, the sensitivity of the KIc and CTOD to each input variable is evaluated using a partial dependence plot (PDP) analysis. While aggregate size, nanofiller content, and a/R ratio have the most positive effect on KIc, aggregates and nanofiller content have the most positive influence on CTOD. Highlights: An experimental database for fracture mechanics of polymer concrete (PC) is prepared.A deep neural network (DNN) model is developed to predict the fracture properties of PC.The effect of seven input variables on KIc and CTOD of PC is evaluated.The sensitivity of KIc and CTOD of PC is analyzed by using partial dependence plots. [ABSTRACT FROM AUTHOR]