Special Issue on Geo-Infrastructure Intelligence: Integrating Artificial Intelligence with Physical Laws for Next-Generation Underground Engineering
Published 02 September, 2026
Introduction
The rapid expansion of urban underground space, including deep tunnels, subway systems, and underground storage facilities, presents unprecedented engineering challenges. Traditional geotechnical analysis and empirical designs often struggle with the high non-linearity, complex geological uncertainties, and dynamic nature of deep rock and soil masses. The emergence of geo-infrastructure intelligence, which integrates artificial intelligence (AI) and machine learning (ML) with physical principles (such as physics-informed neural networks and neural ODEs), offers a feasible path for predictive modeling and structural health monitoring in underground engineering.
This special issue aims to contribute to the discourse on underground engineering within the scientific community. By showcasing pioneering methodologies that combine AI with physics-based geomechanics, the issue provides researchers with actionable frameworks for future exploration into autonomous underground construction. This special issue will also attract high-quality citations, enhance international visibility, and solidify Underground Space's position as a leading forum for next-generation underground technology.
Topics covered
- Physics-Informed Neural Networks (PINNs) applied to rock and soil mechanics
- Neural Ordinary Differential Equations (Neural ODEs) for time-dependent deformation and creep in underground structures
- Integration of data-driven machine learning with Computational Geomechanics (e.g., MPM, NMM, DDA, SPH, DEM, LBM, FEM)
- Intelligent TBM (Tunnel Boring Machine) navigation, performance prediction, and smart tunneling
- Digital twins, big data infrastructure, and real-time structural health monitoring for underground space
- Application of Information Geometry and Non-equilibrium Thermodynamics in smart geotechnics
- Risk assessment and uncertainty quantification in underground engineering using advanced ML algorithms
Important deadlines
- Submission deadline: 31 December 2026
- Publication date: August-October 2027 or as per the journal's policy and plan of the EiC
Submission Instructions
Please read the Guide for Authors on the journal website before submitting. Submission website: https://www.editorialmanager.com/undsp/default.aspx Please select the special issue "VSI: “Geo-Infrastructure Intelligence”
Guest Editors
- Prof. Shanyong Wang, University of Newcastle, Australia, Email: Shanyong.Wang@newcastle.edu.au
- A/Professor Daniela Boldini, Sapienza University of Rome, Italy, Email: daniela.boldini@uniroma1.it
- Prof. Ru Zhang, Sichuan University, China, Email: zhangru@scu.edu.cn
- A/Professor Dominic E.L. Ong, Griffith University, Australia, Email: d.ong@griffith.edu.au
- Prof. Pijush Samui, National Institute of Technology Patna, India, Email: pijush@nitp.ac.in