Machine Learning Applications for Sustainable Infrastructure and Hydrological Modeling
A special issue of AI for Engineering (ISSN 3042-8831).
Deadline for manuscript submissions: 30 June 2026 | Viewed by 517
Special Issue Editors
Interests: sustainable infrastructure & environmental modeling; concrete durability; concrete technologies; AI-driven optimization; Supplementary Cementitious Materials (SCMs)
Interests: sustainable building materials and technologies; management; timber and concrete structures
Special Issues, Collections and Topics in MDPI journals
2. Director of World Sustainable Development Institute, Hong Kong, China
3. Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, China
4. Department of Ocean Sciences, Hong Kong University of Science and Technology, Hong Kong 999077, China
5. Department of Finance, Hong Kong University of Science and Technology, Hong Kong 999077, China
Interests: atmospheric river; east asian monsoon; northwest pacific tropical cyclone; hydrometeorological extremes; regional atmospheric moisture transport & recycle
Special Issue Information
Dear Colleagues,
This Special Issue will focus on the application of machine learning techniques to further advance sustainable infrastructure and hydrological systems. Topics will include predictive modeling of material performance (such as concrete compressive strength), AI‑driven optimization of structural and environmental systems, hydrological forecasting and flood risk assessment, and intelligent monitoring for infrastructure diagnostics. We particularly welcome contributions that integrate explainable AI (XAI) approaches aimed at enhancing transparency and trust in engineering decision‑making, as well as interdisciplinary studies focusing on the ethical and societal implications of deploying machine learning in critical infrastructure.
The aim of this Special Issue is to provide a platform where researchers and practitioners in the field can publish methodological advances, reproducible tools, and case studies that demonstrate transformative opportunities for machine learning in civil and environmental engineering. The Special Issue will bridge the gaps between theoretical foundations and practical implementations of machine learning, promoting cross-disciplinary collaboration in support of the journal's mission to advance AI in engineering design, analysis, and operation.
Dr. Moutaman M. Abbas
Dr. Radu Muntean
Dr. Mengqian Lu
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. AI for Engineering is an international peer-reviewed open access quarterly 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 1000 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
- machine learning in civil engineering
- sustainable infrastructure
- hydrological modeling
- AI-driven optimization
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