Dominant-learner adaptive mixing for concrete compressive strength prediction

Abstract

Accurate prediction of concrete compressive strength is essential for mixture design, quality control, and the broader use of supplementary cementitious materials in low-carbon construction. Fly ash concrete is particularly challenging to model because its strength development is affected by nonlinear interactions among binder composition, water–binder relationships, admixture dosage, and material characteristics. To address this problem, this study proposes a Dominant Learner with Adaptive Mixing (DLAM) framework for data-driven strength prediction. DLAM uses inner cross-validation to identify the most reliable learner from a pool of machine learning models and introduces a validation-controlled Ridge calibration step to exploit complementary information among candidate predictions. The calibration branch is adopted only when it improves the inner-validation root mean squared error (RMSE), thereby reducing the risk of unnecessary model combination and performance degradation. The framework is evaluated using a leakage-free repeated outer/inner validation protocol on a fly ash concrete dataset and is further examined on an independent public concrete strength dataset. DLAM is compared with individual learners, adaptive model-averaging baselines, and Stacking. The results show that DLAM achieves the lowest mean RMSE among the focused comparators on both datasets, with a clear improvement on the external dataset and a more modest gain on the fly ash dataset. These findings demonstrate that validation-controlled calibration provides a transparent and robust way to enhance machine-learning-based concrete strength prediction, especially when different learners capture complementary aspects of the mixture–strength relationship.