An Artificial intelligence approach for predicting compressive strength of eco-friendly concrete containing waste tire rubber

Abstract

Using waste tire rubber as a aggregate replacement in the production of concrete can be considered as an effective way for environment and economies. This study presents an approach based on a prediction model using Artificial Neural Networks (ANN) to predict compressive strength of eco-friendly concrete containing waste tire rubber (RC). A data set with nine influencing features including water, cement, supplementary cementitious materials, coarse aggregate, coarse rubber aggregate, fine aggregate, fine rubber aggregate, superplasticizer, age using for training and validating models have been collected from the literature. The output was compressive strength of RC. The combination of root mean square propagation and stochastic gradient descent with momentum method is employed to train the ANN. Using various validation criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), the ANN model was validated and compared with two machine learning (ML) techniques Random Forest (RF) and Multilayer Perceptron (MLP). A Sensitivity analysis also was carried out to validate the robustness and stability of these models. The experimental results showed that the ANN model outperformed in comparing with other models and therefore it can be used as a suitable approach to predict compressive strength of eco-friendly rubber concrete.