Special Session 2

全球对碳中和与高韧性能源系统的追求势头渐增，加速了地热隧道、地下储能洞库、氢能及压缩空气储能库，以及新兴地下新能源存储与地热利用设施等地质能源基础设施的发展进程。此类系统涉及热 - 水 - 力 - 化学（THMC）多场耦合作用，存在长期不确定性，面临复杂的运营需求。传统岩土工程为其提供了必要的技术基础，而该领域的下一轮技术突破，将依赖于数字智能技术的深度集成 —— 包括先进感知、实时监测、数据驱动建模与自主化系统管理技术。

本专场旨在汇聚岩土工程、岩石力学、地下能源工程、传感技术、计算岩土力学、人工智能、机器人学及数字孪生技术开发领域的科研人员与行业实践者。核心目标是探索跨学科技术方法，借助数字智能技术提升能源基础设施系统（包括新一代地下储能与地热能源系统）的设计、施工、运营及全生命周期管理水平。

现面向全球征集相关领域的摘要投稿，主题包括但不限于以下方向：

（1）先进感知与智能监测 

（2）数据驱动建模

（3）数字孪生与自主运营

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.

优秀摘要将被推荐向以下期刊的特刊提交完整论文：

Underground Space

ENGINEERING Structure and Civil Engineering （原名：Frontiers of Structural and Civil Engineering）

Civil Engineering Design