Digital Twin Construction & City

Aims & Scope

Digital twin (DT) is an integrated multi-physics, multi-scale, probabilistic simulation of an object or an activity, which mirrors the life of its corresponding physical twin. For smart cities and construction, DT can reflect both the semantic and geometric properties and behaviors through virtual models and data to achieve real-time sensing, dynamic control, and information sharing services. A set of innovative information technologies are adopted for the real-time data collection and decision making to develop construction DTs, such as Internet of Things (IoT), blockchain, computer vision, and cloud computing. Real-time data from DTs are shared in cloud space with cyber-physical visibility and traceability to facilitate design, construction, maintenance, and management of smart building and city. Multi-dimensional information including status, location, quality, cost, and safety could be revealed in high-fidelity DTs for stakeholders to monitor the construction progress on a real-time basis. Workers can also safely complete the daily tasks through dynamic control with enhanced information accuracy, timeliness, visibility, and handling efficiency. To make the latest research presented at the 5nd Digital Twin International Conference 2025 (DTIC 2025) widely available, DTIC is extending a Call for Papers for a Special Section reflecting the scope of the conference.


Submissions that reflect the Conference Scope and current state of the field are welcome in areas including but not limited to：

  • • Digital twin-enabled operation and maintenance for smart cities

  • • Novel systematic framework for urban digital twins focusing on transportation networks

  • • Reference model for city digital twins in the energy domain

  • • Semantic digital twins establishing and operation for urban environments

  • • Blockchain-enabled digital twin system for city transportation and energy projects

  • • Computer vision-based generation of urban digital twins for traffic analysis

  • • Digital twins for complex urban infrastructure including transport hubs and renewable energy facilities

  • •Digital twin-enabled robotic and automated urban maintenance for smart transportation and environmental management

  • • Digital twin-enabled urban management for integrated transportation, energy, and environmental systems


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For session presentation applications, please contact the session chairs below:

Ray Y. ZHONG

Email: mailto:zhongzry#hku.hk (replace # with @)

Qian-Cheng Wang

Email: qc.wang#cityu.edu.hk(replace # with @)


Session Chairs

Presentations

  • Postdoc Research Associate
    Stanford University, US
    Digital Twins for Intelligent Intersections: Merging Real-World Data, Probabilistic Modeling, and Monte Carlo Simulations to Improve Safety
    Abstract
  • Postdoctoral Fellow
    MIT, US
    Building the Post-disaster Digital Twin: Large-scale Damage Detection with AI and Remote Sensing
    Abstract
  • Postdoctoral Researcher
    Princeton University, US
    Digital Twins for Intelligent Intersections: Merging Real-World Data, Probabilistic Modeling, and Monte Carlo Simulations to Improve Safety
    Abstract
  • Postdoctoral Research Fellow
    Eindhoven University of Technology, The Netherlands
    Leveraging Digital Twins and Semantic Technologies to Support Circular Decision-Making in the Life Phase of Buildings
    Abstract
  • Postdoctoral Fellow
    Cambridge University, UK
    Maintaining Geometric Building Digital Twins
    Abstract
  • Postdoctoral Fellow
    MIT, US
    Towards Next-Generation Urban Mobility Modeling with Human-Centered Big Data Mining
    Abstract
  • PhD Candidate
    University of Adelaide, Australia
    The Feasibility of iOS-based Automated Construction Progress Monitoring and the Adoption Perceptions of the Technology
    Abstract
  • PhD Candidate
    Tsinghua University, China
    Intelligent design of shear wall layout based on diffusion models
    Abstract
  • PhD Candidate
    Johns Hopkins University, US
    Using Air Drone to Stop Wildfire: A Predict-then-optimize Approach
    Abstract
  • Postdoctoral Fellow
    The Universityty of Hong Kong, China
    AI-Driven Digital Twining Buildings with Window View Semantics for Housing, Planning and Urban Health
    Abstract
  • PhD Candidate
    Carnegie Mellon University, US
    Towards Self-Sufficient and Self-Aware Infrastructure Systems: Model Interoperability and Model Selection in Digital Twin Framework
    Abstract
  • PhD Candidate
    Zhejiang University, China
    AI-enhanced risk forecasting for power transmission systems
    Abstract
  • Postdoctoral Fellow
    MIT, US
    Intelligent Planning with Deep Reinforcement Learning and Urban Simulation
    Abstract
  • PhD Candidate
    The Hong Kong Polytechnic University, China
    Smart Digital Twin System for Fire Evacuation Safety Management
    Abstract
  • PhD Candidate
    Shanghai Jiao Tong University, China
    Multi-Stage Collaborative Optimization: A Key to Minimizing Relocation Rate in Smart Container Terminals
    Abstract
  • PhD Candidate
    Cornell University, US
    Real-World Data-Driven Insights into Cold Climate Impacts on Battery Electric Transit Buses
    Abstract
  • PhD Candidate
    City University of Hong Kong, China
    How new working pattern reshapes space-time behavior: An ontological framework for assessing activity pattern shifts and spatial consequences in UK
    Abstract
  • PhD Candidate
    Peking University, China
    Uncovering the activity-based gender segregation and its association with crime
    Abstract
  • Postdoctoral Associate
    MIT, US
    Digital twin-based spatiotemporal global reconstruction for industrial temperature field: a case study of the snap curing oven
    Abstract
  • PhD Candidate
    University of Adelaide, Australia
    Data-driven digital twins for enhancing regional disaster resilience under climate change
    Abstract
  • PhD Candidate
    City University of Hong Kong, China
    A mini-review of human-centered ontology in city digital twins: From individual behavior to social dynamics
    Abstract
  • PhD Candidate
    University of Adelaide, Australia
    Scalable Health Sensing for Construction Digital Twins: A Data-Driven Framework Based on Fixed PM Monitoring and Activity Context
    Abstract