Driving Sustainability in Civil and Environmental Engineering Through Machine Learning Innovations
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".
Deadline for manuscript submissions: 13 September 2026 | Viewed by 984
Editors
Interests: alkali-activated materials; recycling industrial solid wastes; performance evaluation of concrete; microstructure of concrete; carbon sequestration; durability of FRP; novel construction materials
Special Issues, Collections and Topics in MDPI journals
Interests: concrete; geopolyme; waste; mortar; machine learning
Interests: emission control technologies; biological treatment of air pollutants; fate and transport of emerging contaminants; biohydrogen production; water/wastewater treatment; biological desalination; industrial waste treatment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Civil and environmental engineering are at the forefront of addressing some of the world’s most pressing sustainability challenges, from climate change mitigation and resilient infrastructure to efficient resource utilization and pollution control. In recent years, machine learning (ML) has emerged as a transformative tool in these domains to enable data-driven modeling, predictive analytics, and real-time decision-making that contribute to sustainable engineering solutions.
This Special Issue invites original research articles, state-of-the-art reviews, and case studies focusing on the application of machine learning techniques to promote sustainability in civil and environmental engineering. We welcome multidisciplinary studies and contributions that highlight innovative methodologies, model development, and field implementations aligned with the United Nations Sustainable Development Goals (SDGs).
Papers submitted to this Special Issue should demonstrate how ML contributes to environmentally responsible engineering, efficient system management, and resilient infrastructure design. Submissions integrating artificial intelligence (AI), big data, and automation with practical civil/environmental engineering challenges are also welcomed.
Topics of interest:
Topics include, but are not limited to, the following:
- Machine learning for sustainable materials and green construction practices;
- Predictive maintenance and degradation modeling of infrastructure;
- AI-based energy, water, and waste system optimization;
- Machine learning for air and water quality monitoring;
- Climate risk modeling and adaptation strategies using machine learning;
- Machine learning in structural, geotechnical, and transportation sustainability assessments;
- Integration of GIS and remote sensing with machine learning for environmental diagnostics;
- Carbon footprint and energy consumption prediction using AI;
- Machine learning-powered decision support systems for urban infrastructure planning;
- Deep learning and reinforcement learning in environmental process control;
- Digital twins and machine learning-enhanced BIM for lifecycle sustainability analysis;
- Case studies of machine learning implementation in sustainable engineering projects.
Dr. Hilal El-Hassan
Dr. Abdulkader El Mir
Dr. Ashraf Aly Hassan
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. Sustainability 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 2400 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
- sustainable materials
- resilient infrastructure
- environmental modeling
- artificial intelligence
- smart cities
- climate change
- big data analytics
- life cycle assessment
- predictive maintenance
- environmental engineering
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