Abstract
This study proposes a data-driven experimental decision framework to identify the most suitable sustainable supplementary materials for green concrete, aiming to reduce cement usage, industrial waste burden, and environmental impacts in the construction sector. It experimentally evaluates compressive strength, split tensile strength, flexural strength, and ultrasonic pulse velocity (UPV) of green concrete incorporating waste materials including silica fume, GGBS, metakaolin, granite dust, rice husk ash, ceramic waste, marble powder, coconut shell powder, plastic waste, and bottom ash. A hybrid methodology integrating Pearson correlation, Analytical Hierarchy Process (AHP), and k-means clustering was developed to capture complex interrelationships. Correlation-based dependency analysis was incorporated into AHP to generate objective performance weightages, where compressive strength was ranked highest (37%), followed by flexural strength (25%), UPV (22%), and split tensile strength (16%). K-means clustering then categorized materials into best and worst performance groups. The findings revealed silica fume as the most optimal and balanced material, achieving 48.5 MPa compressive strength, 4.0 MPa split tensile strength, 7.5 MPa flexural strength, and 4400 m/s UPV, indicating superior structural performance and durability potential. ANOVA confirmed strong statistical distinction between clusters (p < 0.0001), validating the robustness of the classification. The main contribution of this work lies in introducing a scalable machine-learning-assisted multi-criteria framework that objectively ranks sustainable cement replacement materials, enabling reliable selection for high-performance green concrete design.