Applications of Artificial Intelligence in Geoenvironmental Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 13
Special Issue Editors
Interests: big data in engineering; machine learning; geospatial data mining
Special Issues, Collections and Topics in MDPI journals
Interests: numerical modeling; foundation engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Geotechnical and geoenvironmental engineering are experiencing a paradigm shift fueled by the integration of artificial intelligence (AI) into conventional engineering practices. With the advent of machine learning, deep learning, physics-informed neural networks (PINNs), and data-driven modeling techniques, researchers and practitioners can now analyze complex subsurface behavior, predict geohazard risks, and optimize infrastructure design with higher accuracy and efficiency. This Special Issue aims to showcase recent advances in the application of AI technologies to solve pressing challenges in geoenvironmental and geotechnical engineering.
We invite submissions of high-quality original research articles, case studies, and critical review papers that explore innovative applications of AI in areas such as slope stability analysis, tunneling risk assessment, settlement prediction, groundwater modeling, and data-driven geotechnical monitoring. Particular emphasis is placed on interdisciplinary studies that combine AI with physical modeling, field data integration, and real-world engineering scenarios. Topics of interest include, but are not limited to, the following:
- Settlement prediction using physics-informed neural networks in geotechnical engineering;
- Deep learning-based slope failure prediction using multi-source and temporal monitoring data;
- AI frameworks for coupled rainfall–groundwater–slope system response analysis;
- AI-assisted tunnel construction risk evaluation in complex geological conditions;
- Machine learning-based inversion of soil parameters and remediation planning;
- Graph neural networks for geotechnical spatial structure analysis;
- Reinforcement learning for optimization of construction processes in underground engineering.
Dr. Suhua Zhou
Dr. Xin Tan
Dr. Minghua Huang
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- geotechnical engineering
- physics-informed neural networks (PINNs)
- slope stability prediction
- tunnel risk assessment
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