Advances in Artificial Intelligence for Geotechnical Engineering
A special issue of Infrastructures (ISSN 2412-3811).
Deadline for manuscript submissions: 31 January 2026 | Viewed by 191
Special Issue Editors
Interests: civil engineering; geotechnical engineering; computational modelling
Special Issues, Collections and Topics in MDPI journals
Interests: geotechnical engineering; construction engineering; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: geotechnical engineering; construction materials; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of artificial intelligence (AI) and machine learning technologies has opened new frontiers across many branches of engineering, including geotechnical engineering. As geotechnical systems often involve complex, nonlinear, and spatially variable conditions, traditional modelling approaches can be limited in their capacity to handle uncertainty, heterogeneity, and large datasets. AI-based approaches provide promising alternatives that can enhance predictive capabilities, improve design efficiency, and enable real-time decision-making in geotechnical engineering practice.
This Special Issue aims to present recent developments, innovative applications, and theoretical advancements in the use of AI for geotechnical engineering problems. It will serve as a platform for researchers and practitioners to share knowledge, foster collaboration, and highlight the role of AI in shaping the future of geotechnical research and practice.
High-quality submissions are invited that address, but are not limited to, the following areas:
- AI-driven modelling of soil behaviour and geotechnical parameters
- Machine learning and deep learning applications in site characterization
- Surrogate and reduced-order models for computationally intensive geotechnical simulations
- Symbolic and interpretable AI (e.g., Genetic Expression Programming, Grammatical Evolution) for geotechnical analysis
- AI-enhanced risk assessment for landslides, foundations, and underground structures
- Integration of sensor data and AI for real-time geotechnical monitoring
- Seismic response prediction using AI methods
- AI-based optimization in geotechnical design and decision-making
- Case studies showcasing practical implementation of AI in geotechnics
Dr. Zia Ur Rehman
Dr. Usama Khalid
Dr. Nauman Ijaz
Guest Editor
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. Infrastructures is an international peer-reviewed open access monthly 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 1800 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
- deep learning
- soil
- soil–structure interaction
- optimization
- constitutive modelling
- numerical modelling
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