Artificial Intelligence Applications in Underground Space Technology
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".
Deadline for manuscript submissions: closed (3 March 2026) | Viewed by 733
Special Issue Editors
Interests: deep learning infrastructure resilience point cloud segmentation; tunnel boring machine; spatial-temporal dynamics in operation
Special Issues, Collections and Topics in MDPI journals
Interests: structural design optimization; digital twins; machine learning; metaheuristics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Underground space technology encompasses the planning, design, construction, operation, and maintenance of subsurface infrastructure, including tunnels, metro systems, underground mines, utility corridors, and underground storage facilities. With accelerating urbanization and the growing scarcity of surface space, the efficient and safe utilization of underground space has become a globally critical focus. Artificial intelligence (AI) is revolutionizing this field by enabling data-driven decision-making, automating complex processes, and enhancing the safety, efficiency, and sustainability of underground projects.
This Topic explores the intersection of artificial intelligence and underground space technology, focusing on how AI techniques—such as machine learning, computer vision, natural language processing, and intelligent optimization—can address challenges unique to subsurface environments. We invite submissions on both theoretical advancements and practical applications, including, but not limited to, the following:
- AI-driven geological exploration and subsurface mapping (e.g., predicting rock properties or groundwater flow using machine learning);
- Intelligent design and optimization of underground structures (e.g., tunnel route optimization via AI algorithms);
- AI-based construction monitoring and control (e.g., real-time risk detection in tunnel boring using computer vision);
- Predictive maintenance and structural health monitoring of underground infrastructure (e.g., employing AI models for early detection of cracks or corrosion);
- Autonomous systems and robotics in underground operations (e.g., AI-powered drones for mine inspection or automated tunnel excavation);
- AI applications in underground space safety management and emergency response (e.g., disaster prediction and evacuation route optimization);
- AI-integrated digital twin technology for underground environments (for simulation and decision support);
- AI-enabled improvement of energy efficiency and reduction in environmental impact in underground projects (e.g., intelligent ventilation control);
- AI-based shield tunnel boring prediction (e.g., using machine learning models to predict tunnel boring parameters such as penetration rate, torque and thrust, and predicting potential geological hazards during the boring process to guide safe and efficient construction).
Dr. Ankang Ji
Dr. Nikos D. Lagaros
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence (AI)
- underground space technology
- machine learning
- tunnel engineering
- underground exploration
- intelligent construction
- structural health monitoring
- underground safety
- digital twin
- underground robotics
- shield tunnel boring prediction
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