ArticleACS omega2026
Machine Learning-Enabled Real-Time Prediction of Drying Shrinkage in Fly Ash-Modified Cementitious Materials.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
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 (
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