Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms

This paper investigates the prediction of carbonation depth for recycled aggregate concrete (RAC) with machine learning models. Nine parameters including RAC intrinsic properties and environmental conditions were considered as input variables. A dataset comprising 593 test data was used to train, validate, and test machine learning models. Results show that the Random forest model shows superior performance than the Gaussian progress regression model and standalone artificial neural network (ANN) model. All ANN models hybridized with swarm intelligence algorithms outperform the standalone ANN model, especially for the ANN model hybridized with the whale optimization algorithm. All machine learning models show higher accuracy than the existing code models and statistical models. The variable importance analysis shows that the carbonation resistance for RAC was determined by both internal and external factors. Based on the parametric analysis, the robustness of the proposed machine learning models was further confirmed.

Automated summary

This paper investigates the prediction of carbonation depth for recycled aggregate concrete (RAC) with machine learning models, where the Random Forest model demonstrated superior performance. ANN models hybridized with swarm intelligence algorithms outperform the standalone ANN model, and all machine learning models show higher accuracy than existing models.