Tunnel Construction and Underground Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 239
Special Issue Editors
Interests: geotechnical engineering; mining engineering; rock mechanics; data analysis using AI and ML; recycling; engineering materials; numerical modeling; simulation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent years have seen significant advancements in tunnel construction and underground engineering, driven by technological innovations such as tunnel-boring machines (TBMs), advanced ground support systems, real-time monitoring techniques, and digital twin modeling. These developments have substantially improved safety, efficiency, and environmental sustainability in tunnel and underground projects globally. Notable progress in this field includes the establishment of smart sensing technologies for structural health monitoring, innovative excavation methods, and advanced geotechnical risk assessment techniques.
Looking ahead, research in this field is expected to further integrate automation, robotics, artificial intelligence, and sustainable practices into tunnel construction and engineering. Future research areas include the development of autonomous tunneling systems, application of artificial intelligence in predictive maintenance, enhanced resilience against geotechnical hazards, and sustainable utilization of underground spaces.
We welcome original research papers, comprehensive reviews, and case studies addressing, but not limited to, the keywords listed below.
Dr. Yuantian Sun
Dr. Haoyu Rong
Guest Editors
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Keywords
- tunnel boring machines (TBMs)
- smart monitoring systems
- ground support innovations
- digital twins in tunneling
- geotechnical risk management
- autonomous excavation
- sustainable underground construction
- artificial intelligence applications
- underground space utilization
- predictive maintenance
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