Artificial Intelligence-Based Prediction of Compressive Strength in High-Performance Eco-Friendly Concrete Incorporating Recycled Waste Glass

This study develops and characterizes a patented eco-friendly engineered cementitious composite (ECC) that incorporates waste glass powder (WGP) and silica fume (SF) as supplementary cementitious materials (SCMs) and recycled glass aggregate (WGA) as an alternative aggregate. Four stages of experimental design produced 14 concrete mixtures tested at 7, 28, 56, 90, and 120 days. Fresh and hardened properties were evaluated, and the optimal mixture, S8-1, A, achieved the requirements of strength class C60/75 and workability with slump class/ consistency class S3. Microstructural analyses using X-ray diffraction and optical microscopy confirmed the formation of secondary hydration products, particularly C-S-H and A-S-H, which contributed to matrix densification and improved performance. To complement the experimental program, an artificial neural network (ANN) was developed to predict compressive strength based on mixture proportions and curing age. Each strength measurement was treated as an independent data point, resulting in 70 samples for model training and testing. A shallow feedforward ANN with three hidden layers was implemented, trained using the Adam optimizer and validated with 10-fold cross-validation. The model achieved high predictive accuracy with R² of about 0.968, mean absolute error of 1.94 MPa, and root mean square error of 2.52 MPa. The results confirm that recycled WGP and SF can be effectively incorporated into ECC while ANN modeling provides a reliable tool for predicting compressive strength and supporting sustainable concrete mix design.