Advances in Machine Learning for the Prediction of Construction Materials Properties
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 10
Special Issue Editor
Interests: machine learning; deep learning and hybrid models; construction materials; material properties prediction; data-driven modeling; soils and unbound aggregates; cementitious composites; asphalt binders and mixtures
Special Issue Information
Dear Colleagues,
Machine learning (ML) is revolutionizing the field of construction materials by offering powerful, data-driven methods to predict a wide spectrum of material properties—ranging from empirical and mechanical to rheological, thermal, and performance-related characteristics. Traditional experimental approaches, though foundational, are often time-consuming, costly, and limited in their ability to capture complex material interactions. ML addresses these limitations by enabling rapid, accurate, and automated predictions using historical data, advanced statistical modeling, and computational algorithms. This Special Issue, “Advances in Machine Learning for the Prediction of Construction Materials Properties”, is dedicated to cutting-edge research that explores the use of ML techniques for modeling and optimizing the properties of critical construction materials, including soils, unbound aggregates, cementitious composites, and asphalt mixtures. Emphasis is placed on how ML can significantly minimize testing time and reduce the reliance on extensive laboratory programs without compromising prediction reliability. Innovations such as deep neural networks, support vector machines, and ensemble learning are capable of modeling nonlinear and multi-factorial behaviors that conventional empirical models cannot capture. These tools are reshaping the way we design, test, and manage materials in civil engineering applications, paving the way for a more sustainable, efficient, and resilient built environment.
Dr. Waleed Abdelaziz Zeiada
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning, deep learning, and hybrid models
- construction materials
- material properties prediction
- data-driven modeling
- soils and unbound aggregates
- cementitious composites
- asphalt binders and mixtures
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