A material-centered predictive framework for strength prediction in fly ash–based cementitious composites using an EAO-ELM hybrid model

Abstract

This study proposes a material-centered predictive framework for fly ash (FA)–based cementitious composites by integrating mixture composition, early-age mechanical properties, and microstructural parameters to model the evolution of compressive strength (CS). A bio-inspired hybrid regression approach, the Enzyme Action Optimizer–enhanced Extreme Learning Machine (EAO-ELM), is introduced to capture the nonlinear relationships governing strength development. In the proposed hybrid model, the random assignment of input–hidden layer weights and biases—the principal source of prediction variance in conventional ELM—is replaced by a population-based global search that minimizes the training loss directly. This improves the conditioning of the hidden-layer output matrix and stabilizes the generalization capability of the model, which explains the performance advantage of EAO-ELM over conventional ELM-based and ensemble learning approaches. Beyond serving as a computational technique, the proposed model provides insight into material behavior and supports performance-driven mixture design. To the best of the authors’ knowledge, this work represents the first application of the EAO to cementitious materials. To systematically evaluate how material-related information influences predictive accuracy, three scenario-based information schemes were constructed. These scenarios enable a comprehensive assessment of the contribution of early-age mechanical properties to long-term strength estimation. Across all scenarios, the EAO-ELM model consistently outperformed classical and ensemble regression methods, achieving an improvement of approximately 36% over the strongest model (Random Forest) in the full-information case. Interpretability analyses using SHapley Additive exPlanations (SHAP) and Partial Dependence Plot further revealed that 7 d CS, cement content, and FA ratio exert the greatest influence on strength prediction, whereas water absorption and ultrasonic pulse velocity (UPV) play secondary roles. Overall, the proposed framework offers a accurate, interpretable, and adaptive means of understanding strength development in sustainable cementitious systems and provides subject to external validation on independent datasets, a promising decision-support aid for optimizing mixture design in construction materials engineering.