The Applications of Artificial Intelligence and Digital Technology in Geotechnical Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".
Deadline for manuscript submissions: 20 July 2026 | Viewed by 3
Special Issue Editors
Interests: tunnel; geological uncertainty; machine learning; spatial variability
Interests: tunnel engineering; intelligent construction
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; intelligent construction; digital twin for geotechnical and underground engineering
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent sensing for underground infrastructure
Interests: Tunnel engineering; numerical modeling in geotechnical engineering; vulnerability sssessment; probabilistic risk analysis; resilience engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Geotechnical engineering faces unprecedented challenges due to increasing infrastructure complexity, climate change impacts, and urbanization demands. Traditional methods often struggle with inherent uncertainties in soil behavior, heterogeneous subsurface conditions, and risk management in mega-projects. The integration of Artificial Intelligence (AI) and Digital Technologies (e.g., IoT, BIM, digital twins, remote sensing) offers transformative potential to revolutionize geotechnical analysis, design, construction, and monitoring.
This Special Issue invites cutting-edge research on AI and digital solutions that enhance the resilience, efficiency, and sustainability of geotechnical systems. We seek contributions that bridge theoretical innovation with real-world applications, addressing critical gaps in:
- Uncertainty Quantification: AI-driven stochastic modeling of soil properties, spatial variability, and multi-hazard interactions.
- Intelligent Construction: Autonomous monitoring, real-time decision support, and risk mitigation for tunnels, deep excavations, and foundations.
- Intelligent Infrastructure: Digital twins for lifecycle management, predictive maintenance, and climate adaptation.
- Cross-Domain Synergy: Fusion of geotechnical data with geospatial, structural, and environmental informatics.
Topics of Interest Include (but are not limited to):
- Machine/deep learning for geological reconstruction and 3D subsurface modeling;
- AI-aided interpretation of geophysical/geotechnical data (e.g., CPT, boreholes, InSAR);
- Digital twins for geostructural health monitoring and early warning systems;
- Robotics and computer vision in site investigation and construction automation;
- Intelligent sensing for geotechnical infrastructure;
- Natural language processing (NLP) for mining geological databases;
- Physics-informed neural networks (PINNs) in soil-structure interaction analysis;
- Big data analytics for urban underground space development;
- Explainable AI (XAI) in geotechnical risk governance;
- Low-carbon geotechnics enabled by AI optimization.
Dr. Jinzhang Zhang
Dr. Jiayao Chen
Prof. Dr. Qiujing Pan
Dr. Jingkang Shi
Dr. Zhongkai Huang
Guest Editors
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Keywords
- artificial intelligence
- digital twin
- machine learning
- intelligent construction
- geotechnical engineering
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