Integration of machine learning and microstructural characterization for strength forecasting with silica fume and M-sand for sustainable concrete.

Incorporating industrial by-products into concrete reduces the environmental impact ofcement production. This study evaluates sustainable ternary concrete mixes containing 10% fly ash, varying silica fume levels (0%, 6%, 12%, 18%, 24%), and 100% manufactured sand as fine aggregate to identify the optimal mix for enhanced mechanical and microstructural properties using scanning electron microscopy (SEM),energy-dispersive spectroscopy (EDS), thermogravimetric analysis (TGA), and machine learning (ML) assessment were done to streamline the experimental process. Compressive, split tensile, and flexural strengths, as well as ultrasonic pulse velocity, were measured at 7, 28, and 90 days. The mix with 10% fly ash, 12% silica fume, and 100% manufactured sand demonstrated the highest performance, with compressive strength increases of 18.61%, 16.85%, and 19.83% at each interval. Microstructural analysis revealed a dense C-S-H gel and uniform matrix, indicating improved hydrationand reduced porosity. Machine learning models (LASSO, Random Forest, Gradient Boosting, XGBoost, AdaBoost, and ANN) were applied to predict compressive strengthand to minimise the number of experimental trials. Gradient Boosting achieved the mostaccurate predictions, with an R