Concrete, as the most widely used construction material, is inextricably connected with human development. Despite conceptual and methodological progress in concrete science, concrete formulation for target properties remains a challenging task due to the ever-increasing complexity of cementitious systems. With the ability to tackle complex tasks autonomously, machine learning (ML) has demonstrated its transformative potential in concrete research. Given the rapid adoption of ML for concrete mixture design, there is a need to understand methodological limitations and formulate best practices in this emerging computational field. Here, we review the areas in which ML has positively impacted concrete science, followed by a comprehensive discussion of the implementation, application, and interpretation of ML algorithms. We conclude by outlining future directions for the concrete community to fully exploit the capabilities of ML models.

3D-printing of cementitious materials is an innovative construction approach with which building elements can be constructed without the use of formwork. Despite potential benefits in the construction industry, it introduces various engineering challenges from the material point of view. This paper reviews the properties of extrusion-based 3D-printed cementitious materials in both fresh and hardened states. Four main properties of fresh-state printing materials are addressed: flowability, extrudability, buildability, and open time, along with hardened properties, including density, compressive strength, flexural strength, tensile bond strength, shrinkage, and cracking. Experimental testing and effective factors of each property are covered, and a mix design procedure is proposed. The main objective of this paper is to provide an overview of the recent development in 3D-printing of cementitious materials and to identify the research gaps that need further investigation.

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.