Machine Learning in Infrastructure Material: Advances and Applications
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".
Deadline for manuscript submissions: 10 November 2026 | Viewed by 524
Special Issue Editors
Interests: multiscale characterization and modeling in infrastructure material; AI-driven infrastructure material design; sensing and data-driven methods for infrastructure monitoring and management
Interests: AI-driven resilient infrastructures
Interests: sustainability; AI-driven decision-making; resilience; civil infrastructure
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The construction industry is undergoing a rapid digital transformation driven by advances in data science and artificial intelligence. Construction materials, as the fundamental components of infrastructure, play a decisive role in structural safety, durability, sustainability, and life-cycle performance. However, traditional construction material design, as well as performance evaluation and prediction methods, often rely on empirical approaches and time-consuming, labor-intensive experiments, which may limit construction efficiency and the design of high-performance materials.
Machine learning (ML) provides powerful tools to model complex nonlinear relationships, integrate multi-source data, and uncover hidden patterns in construction material composition–property relationships. In recent years, ML techniques have been increasingly applied to mix design optimization, mechanical property prediction, durability assessment, microstructure characterization, failure analysis, and life-cycle performance forecasting of construction materials such as concrete, asphalt, composites, metal, and emerging sustainable materials. Data-driven approaches offer new opportunities to accelerate material development, reduce experimental costs, improve performance reliability, and support decision-making in construction projects.
This Special Issue aims to attract research and review articles focusing on the application of ML or data-driven approaches in construction materials. Topics of interest include, but are not limited to, the following:
- ML for predicting the mechanical, thermal, and durability properties of construction materials;
- Physics-informed ML for predicting construction material properties;
- Data-driven mix design and performance optimization of construction materials;
- Sustainable, low-carbon, and recycled construction material design using ML approaches;
- ML-enabled microstructure analysis and image-based construction material characterization;
- ML-enhanced damage detection and residual service life prediction of construction materials.
Dr. Jian Liu
Dr. Fangyu Liu
Dr. Ali Behnood
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- construction materials
- machine learning
- data-driven
- mix design optimization
- performance prediction
- damage detection
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