Materials Informatics and Machine Learning in Pavement Engineering
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".
Deadline for manuscript submissions: 20 February 2025 | Viewed by 184
Special Issue Editors
Interests: material informatics; data mining; pavement sustainability
Special Issue Information
Dear Colleagues,
We are pleased to announce this Special Issue, entitled "Material Informatics and Machine Learning in Pavement Engineering". This Special Issue aims to highlight innovative research at the intersection of material science, informatics, machine learning, and infrastructure engineering. We invite original research articles, reviews, and case studies that explore the use of material informatics and machine learning for the design, analysis, and maintenance of pavement systems. Topics of interest for this Special Issue include, but are not limited to, the predictive modeling of material properties, optimization of pavement materials, data-driven approaches for infrastructure health monitoring, and the integration of machine learning algorithms in material selection and performance prediction. This Special Issue seeks to provide a platform for interdisciplinary collaboration and to advance the application of cutting-edge informatics and analytic tools in enhancing the durability, sustainability, and safety of road pavement infrastructures.
Dr. Jin Li
Dr. Gengren Hao
Guest Editors
Manuscript Submission Information
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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.
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Keywords
- material informatics
- data analytics
- machine learning
- pavement engineering
- construction materials
- material design
- performance prediction
- inverse design
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