Prediction of split tensile strength of recycled aggregate concrete leveraging explainable hybrid XGB with optimization algorithm

Recycled aggregate concrete (RAC) has been adopted in building construction as it can reduce concrete waste, eventually minimizing the environmental impact. However, using recycled materials can lead to compromised performance of mechanical properties like split tensile strength (STS). Several factors, including density, water absorption, and recycled aggregate proportion, play a vital role in assessments of STS. This study explores the prediction of STS by using a hybridized machine learning algorithm in terms of performance indicators. Ensemble model XGB with five optimization algorithms, namely Random search (RS), Grid search (GS), Bayesian Optimization (BO), Grey Wolf optimization (GWO), and Particle Swarm Optimization (PSO), are considered for the study. The comparison shows that XGB-PSO performed very well with R² of 0.999 and 0.960 in the training and testing sets, respectively. The potential performance of GWO is also seen during the assessments. Further, the 10-fold cross-validation used in this study ensures that the models can predict better without overfitting. The model’s explainability is done using Shapley Additive Explanations (SHAP) analysis. SHAP-based study reveals that Cement, Water, and size of aggregates (M-RCA) are critical elements and may enhance STS if considered. The best cement range is 300 to 500 kg/m³, the M-RCA size is 10 to 20 mm, and the water range is 180 to 200 kg/m³. SHAP interaction graphs confirm the result. SHAP interaction graphs confirm a good agreement with the applied range of input parameters for the enhancement of STS with the experimental findings of earlier research.