Modeling the Chloride Migration of Recycled Aggregate Concrete Using Ensemble Learners for Sustainable Building Construction

The use of supplementary cementitious materials such as slag and recycled aggregate in concrete can mitigate some of the negative environmental impacts of using virgin materials. However, the durability of recycled aggregate concrete (RAC) and its resistance to harsh environmental conditions such as chloride penetration must be investigated before practical applications. The rapid chloride migration test (RCMT) is one of the well-established tests that can provide valuable estimations of the concrete quality against chloride penetration. RCMT coupled with machine learning techniques can lead to authentic models, which could save in time, cost, materials, and the need for skilled technicians. In this study, five homogeneous ensemble learners, including two types of bagging and three types of boosting techniques, were developed to model the RCMT output using a comprehensive database collected from the literature. Different types of analysis, including statistical measures, SHapley Additive exPlanations (SHAP) sensitivity analysis, SHAP parametric study, and comparison study, were conducted to examine the performance of the developed models and the effects of the input features on predictions. The results show that the developed extreme gradient boosting learner with the mean absolute percentage errors of about 9% possesses excellent capability for modeling the RCMT of RAC. Besides, the RCMT testing age is the most influential factor affecting the RCMT output, and the amounts of natural fine aggregate and superplasticizer are in the following orders. Finally, a graphical user interface (GUI) was designed, which allows the users to insert the input features and obtain the RCMT output in a user-friendly environment.