Can domain knowledge benefit machine learning for concrete property prediction?

Understanding and predicting process–structure–property–performance relationships for concrete materials is key to designing resilient and sustainable infrastructure. While machine learning has emerged as a powerful tool to supplement empirical analysis and physical modeling, its capabilities are yet to be fully realized due to the massive data requirements and generalizability challenges. To address these limitations, we propose a knowledge-informed machine learning framework for concrete property prediction that aggregates the wealth of domain knowledge condensed in empirical formulas and physics-based models. By integrating the knowledge through data augmentation, feature enhancement, and model pre-training, we demonstrate that this framework has the potential to (i) accelerate model convergence, (ii) improve model performance with limited training data, and (iii) increase generalizability to real-world scenarios (including extrapolation capability to other datasets and robustness against data outliers). The overall improvement of machine learning models by knowledge integration is particularly critical when these models are scaled up to tackle the increasing complexity of modern concrete and deployed in practical applications. While demonstrated for predicting concrete strength, this versatile framework is applicable to a wide range of properties of concrete and other composite materials, paving the way for accelerated materials design and discovery.