Program

Confirmed Special Session

Theme 1

From Sensing to Insight: Collection and Interpretation of Human Factors Data Towards Human-Centric Underground Space

Chairs:

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

Lisha REN, Associate Professor, College of Design and Innovation, Tongji University

Yongkang QIAO, Assistant Professor, College of Civil Engineering, Tongji University

The future of underground space should not be an extension of cold concrete, but a responsive, intelligent, and human-centered organ of the city. While engineering and physical infrastructure have been the traditional focus, the true key to creating safe, efficient, comfortable, and vibrant underground environments lies in a deep understanding of their users: people.

This special session aims to bridge this critical gap. We seek to bring together scholars and practitioners from civil engineering, urban planning, architecture, psychology, ergonomics, and data science. The goal is to explore cutting-edge methods for the precise collection of human factors data and develop interdisciplinary approaches for its meaningful interpretation. We hope to collectively shift the paradigm of underground space design and management from being experience-driven to being driven by data and human-centric insight.

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

(1) Advanced Sensing & Data Collection

Multi-modal data fusion (e.g., computer vision for trajectory analysis, Wi-Fi/Bluetooth for crowd monitoring).
Capturing physiological and psychological signals (e.g., using wearables for EEG, heart rate; innovative surveys and interviews).
Emerging paradigms like Virtual/Augmented Reality for simulation and IoT-based, non-intrusive sensing.

(2) Data Interpretation & Insight Generation

Uncovering behavioral patterns and spatial semantics through data mining.
Affective computing and comfort models linking environmental parameters to subjective well-being.
Human performance and safety assessment for scenarios like emergency evacuation.

(3) Human-Centric Design & Management

Evidence-based design strategies for lighting, wayfinding, and spatial layout.
Promoting health, well-being, and mitigating psychological stress in underground environments.
Smart operation and personalized services using real-time human factors data.

Theme 2

Digital Green Tunnel: Intelligent Sensing, Data Fusion, and System Design

Chairs:

Shouzhong FENG, Professor, School of Civil Engineering and Architecture, Anhui University of Science and Technology

Antonio Peña-García, Professor, Department of Civil Engineering, University of Granada

Yi SHEN, Associate Researcher, College of Civil Engineering, Tongji University

The Digital Green Tunnel represents a next-generation infrastructure that integrates sensing technologies, data analytics, and automated systems to enhance the performance, sustainability, and operational intelligence of tunnel environments. This session focuses exclusively on the technological and data-driven aspects of these advanced systems, moving beyond traditional engineering to explore how digital integration and green solutions can be systematically optimized. It aims to address key challenges in real-time decision-making and resource allocation, establishing a green framework for intelligent, self-adapting tunnel systems. By fostering collaboration across disciplines such as data science, environmental engineering, and intelligent systems, this session seeks to define the future standards for data-driven management in green and digital infrastructure.

We invite contributions on the development and application of sensing, modeling, and management frameworks for Digital Green Tunnels. Abstracts should emphasize technical methodologies, data fusion, and system-level outcomes. Topics of interest include, but are not limited to:

(1) Multi-Modal Sensing & Data Acquisition

Integration of IoT sensors, computer vision, and wireless signals for monitoring environmental and structural conditions.
Deployment of non-intrusive sensing networks for real-time tracking of green infrastructure performance (e.g., plant health, air quality).
Use of VR/AR and digital simulation for modeling tunnel systems and automated responses.

(2) Data Analytics & Model Development

Data fusion and machine learning for predicting environmental dynamics and system behavior.
Pattern recognition in operational data to optimize energy use, airflow, and lighting systems.
Development of digital twin frameworks for scenario testing and predictive control.

(3) Intelligent Design & Automated Management

Data-informed strategies for integrating renewable energy, adaptive lighting, and green infrastructure.
Algorithms for automated operation, maintenance scheduling, and resource allocation.
System architectures enabling real-time control and optimization of tunnel environments.

Theme 3

3Ds in Geo-Sensing: Distributed Sensing, Intelligent Analysis, and Deep Applications

Chairs:

Kai GU, Professor, School of Earth Sciences and Engineering, Nanjing University

Dan ZHANG, Professor, School of Earth Sciences and Engineering, Nanjing University

Jin LIU, Professor, School of Earth Sciences and Engineering, Hohai University

The advent of Distributed Temperature Sensing (DTS), Distributed Strain Sensing (DSS), and Distributed Acoustic Sensing (DAS)—collectively known as “3Ds” technologies—is revolutionizing subsurface characterization and infrastructure monitoring. These techniques provide unprecedented, spatially continuous data on temperature, strain, and vibration across vast scales. This session will explore the complete pipeline from advanced sensing to intelligent data utilization, focusing on the unique challenges and opportunities presented by these massive, high-resolution datasets. We aim to bridge the gap between data acquisition and engineering insight, fostering a transition from simple monitoring to predictive, data-driven decision-making in geo-engineering.

The session will cover novel sensing methodologies, cutting-edge algorithms for big data analysis, and transformative applications across multiple disciplines. By bringing together experts in sensing technology, data science, and geo-engineering, this session seeks to define the future of intelligent ground sensing and its role in creating safer and more resilient infrastructure.

We invite contributions on topics including, but not limited to:

(1) Multi-Physics Sensing & Data Acquisition

Novel deployment techniques and integration strategies for DTS, DSS, and DAS.
Fusion of 3Ds data with other monitoring data (e.g., InSAR, IoT sensors).
New paradigms for real-time, distributed monitoring of geotechnical structures and geological processes.

(2) Intelligent Analysis & Model Development

Machine learning and deep learning algorithms for feature extraction, anomaly detection, and pattern recognition in 3Ds data.
Data compression, denoising, and management strategies for massive distributed datasets.
Development of digital twins and predictive models calibrated with 3Ds data.

(3) Cross-Disciplinary Applications

Applications in geohazard warning (e.g., landslides, land subsidence), geotechnical engineering (e.g., tunnel, slope, and foundation), and hydropower (e.g., dam, dike).
Case studies in energy sectors (e.g., geothermal reservoir, pipeline monitoring) and transportation (e.g., railway, highway).
Long-term performance assessment and lifecycle management of critical infrastructure.

Theme 4

From Sensing to Steering: Multi-Source Data Fusion and Predictive Analytics Towards Intelligent and Autonomous TBM Tunneling

Chairs:

Xu LI, Professor, Key Laboratory of Urban Underground Engineering, Ministry of Education, Beijing Jiaotong University

Mengqi ZHU, Postdoctoral Researcher, College of Civil Engineering, Tongji University

The future of tunnel boring machine (TBM) technology should not be limited to performance and speed, but evolve into a predictive, adaptive, and intelligently orchestrated autonomous system. While engineering advancements and automation have significantly improved TBM capabilities, the true key to achieving safe, efficient tunneling lies in a deep understanding of its core driver: data.

This special session aims to bridge this critical gap. We seek to bring together scholars and practitioners from civil engineering, geotechnical engineering, robotics, artificial intelligence, and advanced manufacturing. The goal is to explore cutting-edge methods for real-time data fusion from geology, machinery, and sensors, and develop AI approaches for predictive analytics and autonomous decision-making. We hope to collectively shift the paradigm of TBM tunneling from being experience-driven to being driven by predictive intelligence and autonomous control.

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

(1) Advanced Sensing & Data Collection

Multi-modal data fusion (e.g., computer vision for trajectory analysis, sound recognition for rock mass identification).
Capturing mechanics and health status signals (e.g., Using distributed optical fiber for structural health status; innovative cutterhead or mechanical health status sensors).
Emerging paradigms like VR/AR-based shield driving and IoT-enabled disturbance sensing.

(2) Data Interpretation & Insight Generation

Uncovering geology, excavation patterns and rock–machine interaction through data mining.
TBM excavation models and training methods with scenario or task generalization capabilities
TBM driving status and risk assessment for scenarios in special geological conditions.

(3) Autonomous Tunneling & Control

Multi-objective model-predictive control trading off energy use, wear cost and schedule deviation in real time.
Promoting interpretability, robustness, and adaptability in intelligent control of TBM excavation.
Enhancing operational efficiency and safety in TBM excavation through decision-making frameworks.

Theme 5

Digital Intelligence Empowered Geo-Energy Infrastructure

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

Theme 6

Artificial Intelligence Enabled Geo-Engineering

Chairs:

Guangqi CHEN, Academician of the Japanese Academy of Engineering, Professor, School of Civil and Transportation Engineering, Hebei University of Technology

Xinzheng LU Professor, School of Civil Engineering, Tsinghua University

Zhenhao XU, Professor, School of Qilu Transportation, Shandong University

Benguo HE, Professor, School of Resources and Civil Engineering, Northeastern University

Xuhai TANG, Professor, School of Civil Engineering, Wuhan University

Gaofeng ZHAO, Professor, School of Civil Engineering, Tianjin University

Zheng HAN, Professor, School of Civil Engineering, Central South University

The rapid digitalization of civil infrastructure and the exponential growth of geological data are reshaping the landscape of geotechnical and geological engineering. As infrastructure projects become increasingly complex and enter more challenging geological environments, traditional empirical and analytical methods often struggle to handle the high dimensionality, non-linearity, and inherent uncertainty of geological conditions. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics offers a transformative solution, shifting the paradigm from experience-based to data-centric engineering.

This special session aims to gather researchers, engineers, and data scientists to discuss the state-of-the-art applications of AI in geo-engineering. The goal is to bridge the gap between advanced computational algorithms and practical geotechnical challenges. We seek to explore how artificial intelligence can automate site characterization, enhance constitutive modeling, optimize design and construction processes, and improve the resilience of geo-structures against natural hazards. Special attention will be given to interpretable AI and physics-informed strategies that ensure reliability and physical consistency in geo-engineering applications.

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

(1) Intelligent Site Characterization & Data Analysis

Computer vision and deep learning for rock mass classification and soil stratigraphy identification.
Subsurface data interpolation and 3D geological modeling using geo-statistics and machine learning.
Handling sparse, noisy, and heterogeneous geotechnical data: Data augmentation and generation strategies.
Automated processing of geo-data and laboratory test results.

(2) Advanced Modeling & Predictive Analytics

Physics-Informed Machine Learning (i.e., PINNs) for solving complex boundary value problems in geomechanics.
Data-driven constitutive modeling and parameter identification for soils and rocks.
Surrogate modeling for real-time prediction of slope stability, tunnel convergence, and foundation settlement.
Generative AI and Large Language Models (LLMs) for geotechnical report analysis and decision support.

(3) Smart Construction & Risk Management

AI-driven optimization for TBM tunneling, drilling, and excavation parameters.
Real-time monitoring and inverse analysis: Updating design parameters based on field performance data.
Intelligent risk assessment and early warning systems for landslides, debris flows, and urban geohazards.
Digital Twin integration: Linking AI algorithms with BIM and sensor networks for lifecycle management.

Theme 7

From Data to Decisions in Complex Engineering Systems

Educational Case Studies at the Intersection of Technology, Management and Human factors

Chairs:

Emma LÓGÓ, Associate professor, Department of Ergonomics and Psychology, Budapest University Technology and Economics

Eszter BÜKKI, Assistant professor, Department of Technical Education, Budapest University Technology and Economics

Engineering practice is undergoing a profound transformation. Advances in sensing technologies, digital modeling, artificial intelligence, and data-driven systems have significantly expanded the technical capabilities of engineers. At the same time, these developments have increased the complexity of engineering decisions, shifting the professional focus from calculation and design alone toward judgment, tradeoffs, and responsible decision-making in sociotechnical systems.

This special session focuses on how future engineers are educated to operate at this intersection of data, technology, and human judgment.

Conceived as a casebased laboratory of future engineering competencies, the session invites educational case studies that demonstrate how realworld engineering problems are used to develop decisionmaking capabilities in complex systems. Contributions may originate from geoengineering, civil and infrastructure engineering, energy systems, industrial and systems engineering, smart cities, or other applied engineering domains where digital technologies and datadriven tools play a central role.

Particular emphasis is placed on:

Transforming data into decisions rather than mere model outputs, teaching system-level thinking across technical, organizational, and human dimensions, and integrating technological tools with managerial, leadership, and professional judgment skills.

The session aligns with the core mission of ICITG by connecting information technology in engineering with its educational and organizational implications, highlighting how universities respond to the evolving demands of modern engineering practice. Building on the strengths of research-intensive institutions such as Tongji University and the Budapest University of Technology and Economics, the session provides a forum for interdisciplinary dialogue on educating engineers for complexity, uncertainty, and responsibility in a digital world.

Theme 8

Sustainability-Driven Geo-Engineering: Transitions, Impacts, and Resilience

Chairs:

Mária Szalmáné CSETE, Associate professor, Department of Environmental Economics and Sustainability, Budapest University Technology and Economics

Attila BUZÁSI, Associate professor, Department of Environmental Economics and Sustainability, Budapest University Technology and Economics

This special session aims to explore the integration of sustainability management and sustainability transitions into geo-engineering systems, with a particular focus on climate change mitigation and adaptation.

While the conference primarily addresses technological advances, this session complements these approaches by incorporating impact assessment, vulnerability analysis, and socio-economic perspectives into data-driven and digitalized geo- engineering solutions. The goal is to support more resilient, sustainable, and informed decision-making in infrastructure planning, design, and operation.

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

Sustainability management in geo-engineering and infrastructure systems
Sustainability transitions in underground and geo-energy infrastructure
Climate change mitigation and adaptation strategies
Environmental and socio-economic impact assessment
Vulnerability and resilience assessment of infrastructure systems
Economic evaluation methods (cost-benefit, cost-effectiveness) for sustainable solutions
Policy, governance, and financing aspects of sustainable geo-infrastructure