Accurate prediction of concrete compressive strength remains a challenging problem due to the complex and nonlinear interactions among mixture components and curing conditions. While machine learning approaches have shown promising results, existing studies are typically limited by static model integration strategies and insufficient consideration of structural relationships among input variables. To address these limitations, this study proposes a novel Adaptive Symmetry-Aware Physics-Informed Hybrid (ASAPH) learning framework. The proposed approach integrates three key components: (i) symmetry-consistent feature representation that preserves invariant relationships among mixture parameters, (ii) a stability-driven feature selection mechanism with a relevance–redundancy trade-off, and (iii) an adaptive input-dependent ensemble strategy that dynamically combines multiple learners. In contrast to conventional stacking methods, the proposed framework employs a learnable weighting function to adjust model contributions based on input characteristics, enabling more flexible, robust, and input-adaptive predictions. The framework combines an attention-based tabular model (TabNet) for representation learning and a tree-based ensemble model (XGBoost) for predictive robustness within a unified adaptive fusion architecture. Experimental results on a benchmark dataset using 10-fold cross-validation demonstrate that the proposed model achieves strong predictive performance, with R2 = 0.9162, RMSE = 4.8271, and MAE = 3.4118, outperforming strong baseline models including XGBoost and TabNet. Furthermore, explainability analysis based on SHAP reveals that curing age, cement content, and water-related parameters are the most influential factors governing compressive strength, consistent with established engineering knowledge. Among these, curing age emerges as the most dominant factor, followed by water-related ratios and cement content, indicating strong alignment with established domain knowledge. These findings confirm that incorporating symmetry-aware and physics-informed representations enhances both interpretability and predictive reliability. Overall, the proposed framework provides a principled and generalizable approach for modeling complex engineering systems, bridging the gap between data-driven learning and physically consistent modeling.
Yılmaz et al. (Tue,) studied this question.