Experimental and machine learning-based analysis of red mud influence on recycled aggregate concrete properties.

This research presents a comprehensive experimental study on the compressive strength and durability performance of environmentally sustainable concrete incorporating red mud (RM) and recycled concrete aggregate (RCA). Throughout all mix designs, the quantities of fine aggregate, silica fume, basalt fiber and water/binder ratios were kept constant, while the cement and coarse aggregate contents were systematically replaced with red mud (0-15%) and recycled concrete aggregates (0-100%) respectively. The experimental results revealed that increasing red mud content led to a consistent increase in compressive strength up to 10% replacement of cement with RM and then showed the decline, with the increment of 5.46% for 0% RA, 12.77% for 50% RA, 18.45% for 75% RA and 19.14% for 100% RA corresponding to the 10% red mud replacement for 28 days curing. Similarly, replacing NCA with RCA caused strength reductions of 22.52% for 50% RA, 38.96% for RA 75% and 57.07% for 100% at 10% replacement of cement with red mud and 28 days of curing, respectively. A similar trend is seen in acid resistance of red mud-based RAC and this reduction reaches 65.1% for 0% RM, 61.7% for 5% RM, 59.44% for 10% RM and 60.62% for 15% RM when recycled aggregates replacement reached from 0 to 100% respectively. However, acid resistance of concrete increased from 0 to 10% inclusion of red mud and then decreased at 15% replacement. In the second phase of the study, the potential of machine learning (ML) algorithms to predict the compressive strength of concrete mixes containing RM and RCA was evaluated. A total of four ensemble-based ML models including random forest (RF), KNN, AdaBoost and XGBoost were trained and tested using 251 datasets for RM and RCA mixes, respectively. Among the models, XGBoost exhibited the highest accuracy and reliability in predicting compressive strength. The study also included an analysis of the influence of individual input parameters on the model's predictive outcomes using SHAP analysis and found the curing age is the most influential parameter while RCA, SF and NCA are the following one.