Topic:

Basic Theory of Resilience of Deep Underground Space in Mega-cities

Organizers:

Xiangsheng Chen, Shenzhen University, xschen@szu.edu.cn
Wenqi Ding, Tongji University, dingwq@tongji.edu.cn
Renpeng Chen, Hunan University, chenrp@hnu.edu.cn
Enzhi Wang, Tsinghua University, nzwang@mail.tsinghua.edu.cn

Introduction:

This session is support by the major project of National Nature Science Foundation of China (No. 52090080) that aims to propose the basic theory of resilience of deep underground space in megacities. The new outcomes of this project will be presented and discussed in this session, mainly including the resilience of geology, underground space system, underground structures, as well as the novel construction technologies to achieve them. Besides the invited lectures from the major project, the related lectures or short spot ideas are welcomed to prompt the Think Tank for Resilience of Underground Space. Basic theories, experimental equipment and results, novel calculation methods, case studies and AI aided methods are the main interesting topics.
This session will be scheduled into two parts: Presentation and Round-table Forum. The presentation part will contain the invited lectures and collected lectures, and the round-table forum is freestyle and leaded by eminent scholars in underground space sector.

Topic:

Cutting-Edge AI Techniques for the Future of Civil Engineering

Organizers:

Fangyu Liu, Tongji University, lfangyu09@tongji.edu.cn
Xiaojun Li, Tongji University, lixiaojun@tongji.edu.cn
Zhoujing Ye, University of Science and Technology Beijing, yezhoujing@ustb.edu.cn

Introduction:

Artificial Intelligence (AI) is reshaping the landscape of civil engineering by enabling smarter, faster, and more resilient infrastructure solutions. This session explores the transformative applications of AI across diverse areas of civil engineering, including structural design, construction automation, structural analysis, structural health monitoring, and infrastructure asset management. Through cutting-edge research presentations and practical case studies, attendees will gain insights into how machine learning, deep learning, computer vision, and data-driven modeling are improving prediction accuracy, optimizing design processes, and enhancing decision-making under uncertainty. Emphasis will be placed on the integration of AI with physics-based modeling, the role of digital twins, and the challenges of data quality, interpretability, and scalability in real-world deployments.
This session invites engineers, researchers, and industry professionals to engage in discussions that will shape the future of civil engineering innovation through artificial intelligence. This session is supported by the Major Science and Technology Project of the Yunnan Department of Transportation (No.202302AD080007) & Ministry of Transport Science and Technology Demonstration Project: Intelligent Construction Technology Demonstration Project for Complex Geology Tunnels on the Yunnan Jinsha River Expressway.

Presentations：

(1) Yuqing Gao: Intelligent design of steel structures driven by the combination of generative artificial intelligence and reinforcement learning.
(2) Ruifeng Luo: Exploration and Practice of Artificial Intelligence Empowering the Construction Industry.
(3) Fangyu Liu: Infrared Thermography and Deep Learning for Pavement Distress Detection.

Topic:

Intermodal Transport and Smart Logistics

Organizers:

Cheng Cheng, Southeast University, chengchengxwz@seu.edu.cn
Yong Zhang, Southeast University, zhangyong@seu.edu.cn

Introduction:

Efficient and resilient intermodal transport – the seamless integration of different transportation modes (road, rail, water, air) – is fundamental to a sustainable global supply chain. Smart logistics, empowered by digitalization, automation, and artificial intelligence, is the key enabler for optimizing this integration. This special session focuses on cutting edge research, innovative technologies, and practical strategies to advance intermodal transport systems and smart logistics operations. We aim to foster discussions on leveraging intelligence to enhance efficiency, reliability, sustainability, and resilience across the entire logistics chain, from planning and execution to monitoring and optimization. Contributions addressing the challenges and opportunities in creating interconnected, data-driven, and automated intermodal logistics networks are highly encouraged.
The following topics will be shown in this session, including but not limited to:
• Intermodal Network & Hub Optimization: Network design, hub location, transshipment efficiency, cost/time/emission trade-offs.
• Intelligent Operations: Real-time scheduling, collaborative management (CTM), resource utilization.
• Sustainable & Resilient Logistics: Low-carbon routing, alternative fuels, emission management, network resilience.
• Smart "Last-Mile" Delivery: Urban distribution optimization for intermodal hubs, crowdsourced logistics, drone/robot delivery, smart parcel lockers, green last-mile solutions

Topic:

Advances in Smart Urban Mobility Technologies

Organizers:

De Zhao, Southeast University, zhaode@seu.edu.cn
Yu Zhou, Beihang University, zhouyu01@buaa.edu.cn

Introduction:

This session invites original research and innovative case studies on the application of advanced technologies to transform urban transportation systems. We seek contributions that demonstrate how Artificial Intelligence (AI), Big Data Analytics, Digital Twins, Internet of Things (IoT), and related technologies address critical challenges in urban mobility. The focus is on developing solutions for more efficient, sustainable, resilient, safe, and equitable transportation networks. We welcome submissions showcasing novel theoretical advances, practical implementations, and evaluations of real-world deployments, covering areas from public transport optimization and intelligent traffic management to predictive modeling and infrastructure resilience.
Topics of Interest (include but are not limited to):
1. Smart Public Transport
2. Intelligent Traffic Management & Control
3. Big Data Analytics for Urban Mobility
4. Artificial Intelligence & Machine Learning Applications
5. Digital Twins for Urban Transportation
6. Mobility-as-a-Service (MaaS) & Shared Mobility
7. Automated & Connected Vehicles in Urban Contexts
8. Other Related Topics

Topic:

Digital Twin Modelling for Autonomous Road Infrastructure and Environment

Organizers:

Prof. Yuqing Zhang, Southeast University, zhangyuqing@seu.edu.cn

Introduction:

Digital twin (DT) for road infrastructure integrate IoT, AI, and Multiphysics modeling to create dynamic virtual replicas of physical assets, enabling real-time monitoring and predictive management. Recent advancements aim to overcome challenges such as data silos, computational limitations, and scalability through innovations like self-powered sensors, hybrid modeling techniques, and edge-AI frameworks. These developments are critical for addressing key issues including pavement deterioration, traffic safety, and climate resilience. Real-world implementations demonstrate DTs’ potential to reduce costs and carbon emissions. The latest research are vital to accelerating the shift toward autonomous and sustainable infrastructure systems. Invited topics include but is not limited to:
(1) Digital Twin Framework for Road Infrastructure and Environment
(2) Self-sensing Technologies & Data Fusion for Real-time Monitoring
(3) Multiphysics/Multiscale Modelling of Road Materials and Structures
(4) Dynamic Data-driven Hybrid Modelling for DT Real-time Updating
(5) High Performance and Edge Computing for AI-aided Predictions
(6) DT Implementations and Demonstrations in Road Environments

Topic:

Multiscale Mechanics and Data-driven Modelling for Climate Resilient Road Infrastructure and Materials

Organizers:

Yangming Gao, Liverpool John Moores University, y.gao@ljmu.ac.uk

Introduction:

Climate change has caused an increase in extreme events (e.g., heat waves, cold snaps, heavy rainfall or snowfall, strong winds, tsunamis, sea level rise, etc.). Road infrastructure is facing more serious challenges due to these extreme events. The road material deterioration and structural distresses are increasing in the frequency and severity under extreme weather and natural hazard conditions. To avoid more frequent pavement maintenance, rehabilitation, and reconstruction, researchers and transportation agencies recently have started to focus on the climate resilience of road infrastructure and materials and are making efforts to transform the road networks to be resilient to climate change. As the kernel of performance prediction of road structures and materials, multiscale mechanics and data-driven modelling lay down a solid foundation for material selection, design and evaluation of resilient road infrastructure. Innovative research in multiscale mechanics and data-driven modelling provides an insight into understanding the mechanisms, properties and performance of resilient road structures and materials at multiple scales. The topics of interest include but are not limited to:
1. Characterisation of extreme weather events caused by climate change 2. Natural hazards’ interactions with road infrastructure
3. Impact of coastal hazards on port pavements and coastal roads
4. Multiscale, multiphase, and multiphysics resilience modelling of paving materials
5. Spatial data and AI for road resilience assessment and optimisation
6. Nature-based solutions for enhancing road infrastructure resilience
7. Climate-adaptive road infrastructure management strategies and their sustainability