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

Abstract

Recycled aggregate-based concrete 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 better evaluation of STS using a hybridized machine learning algorithm. Ensemble model XGBoost with ve 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 R 2 of 0.9988 and 0.9602 in the training and testing sets, respectively. The potential performance of GWO is also seen during the assessments. Further, the 10-fold crossvalidation used in this study ensures that the models can predict better without over tting. 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/m 3 , the M-RCA size is 10 to 20 mm, and the water range is 180 to 200 kg/m 3 . SHAP interaction graphs con rm the result. This study helps engineers and researchers to understand the critical parameters for making informed decisions, thus promoting sustainable construction practices.