Integrating Machine Learning with Concrete Science: Bridging Traditional Testing and Advanced Predictive Modelling

This paper thoroughly explores the application of machine learning (ML) in concrete science, bridging traditional testing methods with advanced ML techniques. It begins with an overview of ML fundamentals and their relevance to concrete materials, highlighting ML's transformative potential in enhancing predictive modelling and analysis. The discussion covers various ML techniques, including supervised, unsupervised, and deep learning, along with common algorithms and models used in concrete research. Practical aspects such as data collection methods, preprocessing techniques, and feature engineering specific to concrete science are detailed, illustrating how ML improves the accuracy and efficiency of predicting properties like compressive strength, durability, and workability. The paper also examines challenges such as data quality, model interpretability, and scalability, and discusses future trends, ethical considerations, and the societal impacts of ML applications in advancing sustainable infrastructure.