Explainable machine learning framework for drying shrinkage prediction in treated recycled aggregate concrete

Abstract This study presents the first explainable machine learning (ML) framework for predicting drying shrinkage in treated recycled aggregate concrete, addressing a critical knowledge gap hindering structural applications. A comprehensive database of 460 concrete mixes from 30 peer‐reviewed studies (2015–2024) was compiled, encompassing seven recycled concrete aggregates (RCA) treatment strategies and replacement levels of 25%–100%. Five ML algorithms were optimized and integrated with explainable Absorption Index techniques (SHapley Additive Explanations, Local Interpretable Model‐Agnostic Explanations, partial dependence plots) to provide both predictive accuracy and mechanistic transparency. Extreme Gradient Boosting achieved exceptional performance ( R 2 = 0.974, root mean square error = 12.25 με, mean absolute error = 9.29 με), reducing prediction errors by 55% compared to classical formulations (ACI 209R, B3, Comité Euro‐International du Béton – Fédération Internationale de la Précontrainte [CEB‐FIP]) while maintaining errors below experimental measurement uncertainty (±20–30 με). Explainability analyses revealed that curing age and natural aggregate content dominate shrinkage evolution (>85% variance), while treatment‐specific thresholds were identified: mechanical and thermal treatments remain effective up to ~400 kg/m 3 (~50% replacement), whereas chemical treatments should be limited to