Special Session 2

Chairs

Feng ZHANG, Professor, College of Civil Engineering, Tongji University

Ákos TÖRÖK, Professor, Department of Engineering Geology and Geotechnics, Budapest University of Technology and Economics

Fang LIU, Professor, College of Civil Engineering, Tongji University

Kövesdi BALÁZS, Professor, Department of Structural Engineering, Budapest University of Technology and Economics

Jiao-Long ZHANG, Associate Professor, College of Civil Engineering, Tongji University

The global pursuit of carbon neutrality and resilient energy systems is accelerating the development of geo-energy infrastructure, including geothermal tunnels, underground energy-storage caverns, hydrogen and Compressed Air Energy Storage (CAES) reservoirs, and emerging subsurface facilities for new energy storage and geothermal utilization. These systems involve highly coupled thermo–hydro–mechanical–chemical (THMC) processes, long-term uncertainty, and complex operational demands. While traditional geotechnical engineering offers essential foundations, the next breakthroughs will be driven by the integration of digital intelligence—advanced sensing, real-time monitoring, data-driven modeling, and autonomous system management.

This special session seeks to bring together researchers and practitioners from geotechnical engineering, rock mechanics, underground energy engineering, sensing technology, computational geomechanics, artificial intelligence, robotics, and digital twin development. The goal is to explore interdisciplinary methods that leverage digital intelligence to enhance the design, construction, operation, and lifecycle management of geo-energy infrastructure, including new-generation underground energy storage and geothermal energy systems.

We invite abstract submissions on topics including, but not limited to:

(1) Advanced Sensing & Intelligent Monitoring

• Multi-modal and multi-physics sensing for THMC processes in geo-energy systems

• Distributed fiber optics, micro-seismic/acoustic  monitoring, and thermal–hydraulic sensing

• Robotics-assisted inspection and autonomous data collection in underground environments

• Real-time data fusion, anomaly detection, and early-warning strategies

(2) Data-Driven Modeling & Insight Generation

• Physics-informed machine learning (PINNs, operator learning) for THMC modeling

• Scan2BIM, BIM2FEM, and digital workflows for geo-energy facilities

• Big benchmark datasets, uncertainty quantification, and performance prediction

• Intelligent geomaterials and material informatics for energy harvesting and storage

(3) Digital Twins & Autonomous Operations

• Digital twin platforms for geothermal tunnels, hydrogen/CAES storage, and deep geo-energy structures

• Reinforcement learning and LLM-based decision tools for adaptive control and risk mitigation

• Autonomous operation and maintenance strategies to enhance safety and resilience

• IT-enabled sustainability assessment, life-cycle optimization, and predictive management