Development of deep neural network model to predict the compressive strength of rubber concrete

This paper presents an innovative development process of a Deep Neural Network model to predict the compressive strength of rubber concrete. To this goal, a rubber concrete database is carefully constructed, incorporating a set of binder, aggregate, and other related concrete variables as input parameters, whereas the compressive strength is considered as output. The development of the DNN model includes extensive analysis of the number of hidden layers and the neurons in each layer, combining with a statistical investigation of the models' prediction outputs. The results show that the DNN model outperforms other neural network structures according to several well-known performance indices, such as coefficient of determination, root mean square error, and mean absolute error. The proposed DNN model also exhibits higher prediction accuracy than previously published results, using different machine learning algorithms in the literature. A sensitivity analysis using partial dependence plots is performed within the DNN algorithm in order to achieve an in-depth examination of the influence of each single input variable on the predicted compressive strength of rubber concrete. Finally, the possibility of using other input variables is given to pave the way for applications in regular, high strength, or light-weight foamed concrete containing rubber particles. CO 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study successfully predicts the compressive strength of rubber concrete through an innovative DNN model development process, demonstrating high accuracy and performance. By carefully constructing a database and conducting in-depth analysis, the DNN model outperforms other neural network structures, showing potential for wide applications.