Assessment of the split tensile strength of fiber reinforced recycled aggregate concrete using interpretable approaches with graphical user interface

Incorporating recycled concrete aggregates into concrete represents a sustainable approach to mitigate the extraction of natural mineral resources and alleviate the adverse environmental effects associated with the concrete industry. Nevertheless, it encounters challenges due to the vulnerability of the hardened mortar adhering to natural aggregates. Thus, leading to an increased susceptibility to cracking and reduced strength. Therefore, this study utilized machine learning (ML) methods such as gene expression programming (GEP), random forest regression (RFR), artificial neural networks (ANN), ANN-Bagging, and ANN-Boosting models to forecast the split tensile strength (STS) of fiber-reinforced recycled aggregate concrete (FRRAC). To develop the models, 257 data points were gathered from experimental studies, encompassing ten influential input variables and considering the STS as the output variable. The accuracy of the models was assessed using statistical metrics. The five developed prediction models showcased excellent and reliable performance, with an impressive correlation coefficient (R) exceeding 0.80 during training and reaching 0.92 in testing. However, the GEP model outperformed the remaining four models, with an excellent R-value of 0.980 in training and 0.983 in testing. In addition, the standalone ANN model demonstrated performance on par with the ensemble ANN-Boosting model. In contrast, the ANN-Bagging ensemble model outperformed the individual ANN model, showcasing superior prediction accuracy with impressive R-values of 0.952 and 0.975 for training and testing, respectively. Moreover, the feature analysis based on SHapley Additive exPlanations (SHAP) revealed that fiber content, recycled aggregate density, cement, and water have the highest contribution in estimating the STS of FRRAC. Furthermore, a user-friendly graphical interface (GUI) has been developed to simplify the application of machine learning models in predicting the strength properties of concrete. In conclusion, the findings can encourage the utilization of FRRAC in both structural and non-structural reinforced concrete elements. Therefore, promoting the recycling and reuse of construction waste into building materials and thereby reducing environmental impact. Furthermore, incorporating recycled concrete aggregate in construction projects has the potential to decrease construction material costs, as it reduces reliance on natural resources such as natural coarse aggregate.