A material-independent deep learning model to predict the tensile strength of polymer concrete

This study intends to predict the tensile strength of polymer concrete (PC) composites using one of the deep learning-based methods called deep neural network (DNN). A database including 9 variables and the corre-sponding tensile strength, which consists of 281 experimental data from 22 previous works is prepared. These variables are the weight percentage of materials and also dimensions of fillers, which are used as input variables of the DNN system, and the corresponding tensile strength is the output. The performance of the developed DNN model is evaluated using statistical criteria R, R2, MSE, RMSE, and MAE. Afterward, the sensitivity of the tensile strength of the PC to each input variable is investigated using a partial dependence plot (PDP) analysis within the obtained DNN model. The positive and negative influence of each input variable on the tensile strength can be implemented in the optimized design of the composition of the PC.

Automated summary

This study predicts the tensile strength of polymer concrete composites using a deep neural network method. A database with 9 variables and 281 experimental data is prepared, and the model performance is evaluated using statistical criteria. The sensitivity of each input variable on the tensile strength is explored through a partial dependence plot analysis.