Modeling the mechanical properties of recycled aggregate concrete using hybrid machine learning algorithms

Abstract

To explore the complicated functional relationship between key parameters such as the recycled aggregate properties, mix proportion and compressive strength of recycled aggregate concrete (RAC), a complete database involving 607 records from relevant published literature was built. Two standard algorithms (artificial neural network (ANN) and support vector regression (SVR)) and two optimized hybrid models (Particle Swarm Optimization based SVR (PSO-SVR) and grey Wolf optimizer based SVR (GWO-SVR)) were adopted. Furthermore, two interpretable algorithms (Partial Dependence Plot (PDP) and SHapley Additive exPlanations (SHAP)) were utilized to assess the global and local approaches independent of machine learning models, contributing towards decision-making rationales. Results indicated that the coefficient of determination (R2) of ANN, SVR, PSO-SVR and GWO-SVR were 0.7569, 0.5914, 0.8995 and 0.9056 respectively, showing that hybrid models outperformed the conventional models. However, GWO-SVR was the most problematic with overfitting when analyzing its three subsets. The two feature importance analyses revealed cement content, water content, natural fine aggregates, and water absorption as significant characteristics that affect mechanical performance.