Machine Learning and Artificial Intelligence in Geotechnical and Underground Infrastructures
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".
Deadline for manuscript submissions: 25 May 2025 | Viewed by 68
Special Issue Editors
Interests: data-driven geotechnics; tunnelling; digital twin in geotechnical and underground engineering
Special Issues, Collections and Topics in MDPI journals
Interests: tunnel and underground space, shield tunneling, soil-structure interaction
Interests: underground construction; risk assessment; ground improvement
Special Issues, Collections and Topics in MDPI journals
Interests: municipal solid waste; construction solid waste; excavated soil; slope stability
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The modern engineering industry has erected many geotechnical infrastructures (e.g., slopes, tunnels, sewers, subway stations, and deep foundations), facing many uncertainties in geological formation and construction workmanship and simultaneously producing a large amount of multi-source and heterogeneous data during the planning, design, construction, and maintenance of these underground assets. In the era of Industry 4.0, engineers and researchers in the civil community have become more interdisciplinary and are required to harness the potential of machine learning, artificial intelligence, and information modeling techniques to provide novel solutions to new challenges in the design, construction, and maintenance of underground engineering.
To advance the development and application of machine learning and artificial intelligence in geotechnical and underground engineering, and to enhance its digital, information, and intelligence capabilities, Sustainability presents a Special Issue that focuses on the application of machine learning and artificial intelligence technologies in the design, construction, operation, and maintenance of geotechnical and underground infrastructures.
Prof. Dr. Qiujing Pan
Guest Editors
Dr. Dalong Jin
Dr. Yutao Pan
Dr. Hui Xu
Co-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 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. 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
- data-centric geotechnics
- geotechnical risk assessments and management
- computer vision in geotechnical engineering
- building information modeling and digital twin
- integration of data-driven and physics-based methods
