Hybrid machine learning models for predicting the tensile strength of reinforced concrete incorporating nano-engineered and sustainable supplementary cementitious materials.

Despite its significance for performance-based design, concrete's tensile strength, which governs crack formation, structural damage, and the longevity of the structure, has usually received inadequate attention in predictive modeling compared to compressive strength. This paper proposes a complete machine learning approach to forecast the tensile strength of nano-engineered and sustainable concretes utilizing a novel database containing 500 data points and ten diverse input factors such as water-to-cement (w/c) ratio, curing time, coarse and fine aggregates, and content of other additives (basalt fiber, carbon nanotube, geopolymer binder, superplasticizer). The study employed and compared four state-of-the-art machine learning algorithms, including support vector regression (SVR), artificial neural networks (ANN), extreme gradient boosting (XGBoost), and a novel hybrid ensemble model (HEM) that uses a weighted meta-regressor to combine outputs from multiple base learners. The HEM model consistently outperformed the others in hold-out cross-validation, K-fold cross-validation, and generalization tests. It achieved the best predictive accuracy with a K-fold cross-validation composite score of 96. ANN came in second place, followed by SVR and XGBoost, with K-fold cross-validation ranking scores of 70, 53, and 25, respectively. In the generalization tests, HEM demonstrably followed the nonlinear trends in the tensile strength for w/c ratios (peak at 0.4 with 36.95 MPa), curing time (peak at 56 days with 38.49 MPa), and nano-clay content (peak at 5% with 38.86 MPa), surpassing the other models in every case. Sensitivity analysis using the HEM model revealed optimal input values for maximizing tensile strength, including cement content (378.7 kg/m