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AI for Engineering
Special Issues
Machine Learning Applications for Sustainable Infrastructure and Hydrological Modeling
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Machine Learning Applications for Sustainable Infrastructure and Hydrological Modeling

A special issue of AI for Engineering (ISSN 3042-8831).

Deadline for manuscript submissions: 12 March 2027 | Viewed by 2536

Editors

Dr. Moutaman M. Abbas

E-Mail Website
Guest Editor
Department of Civil Engineering, Transylvania University of Brașov, Bulevardul Eroilor 29, 500036 Brașov, Romania
Interests: sustainable infrastructure and environmental modeling; concrete durability; concrete technologies; AI-driven optimization; supplementary cementitious materials (SCMs)
Special Issues, Collections and Topics in MDPI journals
Dr. Radu Muntean

E-Mail Website
Guest Editor
Department of Civil Engineering, Transilvania University of Brasov, 500036 Brasov, Romania
Interests: sustainable building materials and technologies; management; timber and concrete structures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mengqian Lu

E-Mail Website
Guest Editor
1. Director of Otto Poon Center for Climate Resilience and Sustainability, Hong Kong, China
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-anonymized 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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Published Papers (2 papers)

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17 pages, 3035 KB  
Article
Machine Learning-Assisted Estimation of Carbon Emissions from Data Centers: A Case Study of the New York City Metropolitan Region
by Ji Kim and Jaeyoung Jay Sun
AI Eng. 2026, 1(2), 7; https://doi.org/10.3390/aieng1020007 - 15 Jul 2026
Viewed by 466
Abstract
This pilot study presents a surrogate modeling framework for estimating carbon emissions for 35 data centers in the New York City metropolitan area. Using publicly available facility data (square footage, operator type, location), we calculated the annual CO2e emissions based on [...] Read more.
This pilot study presents a surrogate modeling framework for estimating carbon emissions for 35 data centers in the New York City metropolitan area. Using publicly available facility data (square footage, operator type, location), we calculated the annual CO2e emissions based on standard industry assumptions. These calculated values, which represent modeled emissions rather than measured data, served as the target variable for surrogate model development. A Random Forest regression model was implemented. The model achieved strong performance in producing the calculated emissions with the test set with cross-validated performance (CV R2 = 0.960 ± 0.022 and CV MAE = 2431 ± 739 MT CO2e). Analysis indicated that data center size was the major predictor, accounting for 79.7% of the total feature importance, while location and operator type contributed 13.6% and 6.6%, respectively. As a localized, preliminary feasibility study, this case study demonstrates that surrogate modeling using only publicly available facility data can provide modeled carbon footprint estimates for infrastructure planning and grid decarbonization efforts. The reproducible methodology can be applied to other metropolitan regions, though generalizability requires further validation with larger datasets. Full article
(This article belongs to the Special Issue Machine Learning Applications for Sustainable Infrastructure and Hydrological Modeling)
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Other

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19 pages, 2349 KB  
Technical Note
Agentic SWMM: Auditable and Reproducible Stormwater Modelling Workflow with Agent Skills and Model Context Protocol
by Zhonghao Zhang and Caterina Valeo
AI Eng. 2026, 1(1), 5; https://doi.org/10.3390/aieng1010005 - 9 Jun 2026
Viewed by 1195
Abstract
Configuring urban hydrological models, such as the Storm Water Management Model (SWMM), for operational use remains onerous for many modellers. We propose aiswmm, a SWMM-specialized agentic runtime, together with an Agentic SWMM workflow that embeds (Agent) Skills and Model Context Protocol (MCP) tools [...] Read more.
Configuring urban hydrological models, such as the Storm Water Management Model (SWMM), for operational use remains onerous for many modellers. We propose aiswmm, a SWMM-specialized agentic runtime, together with an Agentic SWMM workflow that embeds (Agent) Skills and Model Context Protocol (MCP) tools to automate QGIS preprocessing, SWMM configuration, execution, and postprocessing. We demonstrate this natural-language triggered workflow on the Tod Creek watershed (located on the Saanich Peninsula, British Columbia). We also validate the proposed Agentic SWMM workflow at three levels: (i) a QGIS-based watershed-pour-point detection that agrees with the commercial PCSWMM® method to within 0.88% of the watershed perimeter (approximately 7.5 pixels in the digital elevation model); (ii) byte-identical SWMM output files (Secure Hash Algorithm 256-bit identical) between the command-line execution and the MCP paths across 60 paired simulations, and (iii) peak inflow at the watershed outlet matching to three significant digits between the manual SWMM interface and Agentic SWMM workflows. The results confirm that Agentic SWMM workflow can produce the same outputs with the manual SWMM interface, as they are designed to use the same computational engine. We also propose a verification-first contract and byte-level audit chain that record the inputs, parameters, and outputs of each run, thereby supporting the auditability and reproducibility. The aiswmm runtime, Skills, MCP servers, and byte-level audit chain are released as open source and remain compatible with mainstream agentic runtimes (Codex, Claude Code, Hermes, and OpenClaw) to support reproducible SWMM modelling driven by natural language. Full article
(This article belongs to the Special Issue Machine Learning Applications for Sustainable Infrastructure and Hydrological Modeling)
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