Random forest and Shapley additive explanation for compressive strength prediction of NaOH-pretreated crumb rubber concrete

Abstract

The compressive strength (CS) of crumb rubber concrete (CRC) can be improved through a chemical pretreatment involving immersion of the crumb rubber particles in a NaOH solution. Despite the potential benefits of this treatment, accurately estimating the CS value of NaOH-pretreated CRC remains challenging. To address this issue, a comprehensive database encompassing 118 entries on the fundamental mixtures of CRC along with NaOH concentration and pretreatment duration has been meticulously compiled for machine learning analysis. The random forest (RF) algorithm is employed to predict the 28-day CS value of NaOH-pretreated CRC. The model hyperparameters are optimized using a random search technique with 10-fold cross-validation. The findings reveal that the optimized RF attains acceptable predictive performance, yielding RMSE, MAE, and R2 values of 3.83 MPa, 2.84 MPa, and 0.85, respectively, on the testing dataset. Additionally, the model is interpreted using multiple techniques, including permutation importance, RF model-based feature importance, and Shapley additive explanation from global or local perspectives. The feature importance analyses consistently highlight the crucial role of crumb rubber content in determining the 28-day CS value of NaOH-pretreated CRC, and the influence of NaOH concentration and pretreatment time appear relatively inconsequential compared to features associated with the CRC mixture. This research contributes to a deeper understanding and better mixture design of CS for NaOH-pretreated CRC.