Submission Deadline: 31 May 2026 View: 590 Submit to Special Issue
Prof. Dr. Zhanping Song
Email: songzhpyt@xauat.edu.cn
Affiliation: School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055 China
Research Interests: intelligent geotechnical engineering, smart construction of urban underground spaces, rock mechanics and engineering, tunnels and underground engineering
Dr. Alessio Fumagalli
Email: alessio.fumagalli@polimi.it
Affiliation: Department of Mathematics, Politecnico di Milano Piazza Leonardo da Vinci 32, 20133 Milano, Italy
Research Interests: numerical analysis, flow in porous media, reduced order modeling for fractures, VEMX, FEM
Dr. Zhilin Cao
Email: caozhilin@xauat.edu.cn
Affiliation: Institute for Interdisciplinary and Innovation Research, Xi'an University of Architecture and Technology, Xi'an, 710055 China
Research Interests: geotechnical mechanics, discontinuous numerical simulation methods, engineering geological hazard prevention and control
Dr. Xiaole Shen
Email: shenxiaole@xauat.edu.cn
Affiliation: School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055 China
Research Interests: discrete element simulation, rock mechanics, geotechnical hazards
The integration of Artificial Intelligence (AI) technology with advanced numerical simulation methods is profoundly transforming research paradigms in tunnel and underground engineering. Traditional numerical methods, such as Finite Element Method (FEM), Discrete Element Method (DEM), and fluid-solid coupling numerical methods, are increasingly being deeply integrated with AI tools, including Machine Learning (ML), Deep Learning (DL), and neural networks, to achieve more accurate, efficient, and robust predictions for tunnel construction processes and long-term operational performance.
This special issue aims to showcase the latest advancements in research at the intersection of AI and numerical modeling in tunnel engineering, with particular attention to key issues such as intelligent modeling, construction safety assessment and risk prediction, environmental impact control, and long-term performance evaluation of underground structures. We invite original research and high-quality review articles in the field of tunnel engineering, especially those proposing innovative AI-enhanced numerical models, hybrid simulation methods, and data-driven tunnel engineering analysis techniques to address current challenges in the design, construction, and operation of tunnel engineering.
Topics may include but are not limited to:
· Machine learning-enhanced numerical analysis methods for tunnel engineering
· Applications of artificial intelligence in rock stability prediction and disaster early warning for tunnels
· Integration of deep learning with traditional numerical methods for tunnel construction process control
· Data-driven structural health monitoring and long-term performance evaluation of tunnels
· AI-assisted modeling of groundwater and soil-structure interaction issues in tunnel engineering
· AI methods for uncertainty quantification and risk management in tunnel engineering
· Intelligent numerical solutions for multi-scale, multi-physics coupling problems in tunnel construction processes
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