Machine Learning-Enabled Real-Time Prediction of Drying Shrinkage in Fly Ash-Modified Cementitious Materials

Drying shrinkage remains a critical durability challenge in cementitious materials, where conventional prediction methods are limited by low temporal resolution and lack of interpretability. This study presents an interpretable machine learning (ML) framework for real-time prediction of drying shrinkage in fly ash-containing concrete, integrating high-frequency experimental measurements with physically meaningful input features. A data set comprising 79,008 hly observations across 16 mix designs and different curing conditions was used to train and evaluate various ML models. Ensemble and nonlinear models, including Random Forest, Extra Trees, K-Nearest Neighbors, and Multi-Layer Perceptron, achieved high predictive accuracy (R ² ≈ 0.99) on the test set with low error margins under both hourly and daily temporal representations. Besides, SHapley Additive exPlanations (SHAP) analysis reveals that multiscale temporal variables govern shrinkage evolution, with short-term capillary moisture loss and long-term pore-scale desorption captured through distinct temporal features. Material parameters, particularly fly ash incorporation, exhibit secondary but consistent influence through microstructural refinement mechanisms based on C-S-H phase nucleation and formation. The proposed framework enables a transition from periodic testing toward continuous, data-driven monitoring, offering a pathway for real-time quality control and reduced physical testing of sustainable concrete infrastructures.