Tuesday, October 15th 2024
(Milan Time) 14:00-17:00
(Beijing Time) 20:00-23:00
Tencent ID : 258-954-982
Session Aims & Scope
As the biggest global industry sector, the construction industry has become digitally lagging compared to other industry sectors. Both academics and practitioners have recognized the demand for the digital transformation of the construction industry. Hence, smart construction is crucial to support the digital transformation of the construction industry. How to use emerging information technologies and concepts to enable smart construction is an important question to ensure the safety and efficiency of the construction industry. To share the latest advances in DT technologies for smart construction, the Digital Twin International Conference in 2024 (DigiTwin 2024), is extending a Special Session for Digital Twin Driven Smart Construction. The proposed Special Session particularly fits the following topics, but are not limited to:
- Digital twin modeling and simulation for smart construction
- Digital twin for smart construction quality control
- Digital twin for smart construction safety control
- Digital twin for smart civil infrastructure operation and maintenance
- Digital twin for risk prediction and control
- Development of AI-driven digital twin technologies in construction
- Digital twin for smart equipment and robotics
Session Chair(s)
Chair
Zhansheng LIU
Professor
Beijing University of Technology (China)
Co-Chair
Zhe SUN
Assistant Professor
Beijing University of Technology (China)
Co-Chair
Fengjuan CHEN
Associate Professor
Beijing University of Technology (China)
Session Presentation
1.
Kefu LV
Engineer
China National Nuclear Corporation (China)
Title: Enhancing Nuclear Power Production with Digital Twin and Artificial Intelligence
Abstract
Integrating artificial intelligence (AI) with digital twin technology offers transformative potential for enhancing nuclear power production. Digital twins create precise virtual models of physical assets, while AI enhances these models with real-time data analysis, predictive maintenance, and process optimization. This combination boosts operational efficiency, safety, and reliability in nuclear power plants. AI-driven insights, supported by digital twins, can streamline maintenance, optimize energy output, and improve overall plant management. As advancements continue, particularly in explainable AI, the synergy of these technologies is poised to revolutionize nuclear energy, driving innovation and regulatory developments in the industry.
2.
Yu ZHANG
Senior Engineer
China Institute of Atomic Energy (China)
Title: Digital Twins Supporting Decommissioning of Heavy Water Research Reactor
Abstract
Heavy Water Research Reactor (HWRR) is the first reactor in China. According to the plan of China Institute of Atomic Energy (CIAE), immediate dismantling is selected as the strategy of HWRR decommissioning. Nuclear reactor decommissioning is a complex engineering. Digital Twins of nuclear facilities recreate a facility’s technology and structures and support effective design, operation, and maintenance. This research puts forward digital twins technology application in HWRR decommissioning, supporting the characterization, dismantling, and radioactive waste management.
3.
Shen ZHANG
Chairman, Zhongda Digital Company
Digital Director, Central South Architectural Design Institute (China)
Title: Solution of Digital Twin Application in Supply Chain of Manufacturing Enterprises
Abstract
An intelligent construction platform is built with industrial software as the core to revolutionize the traditional project management, break down data silos, realize the whole process collaboration, and “one model to the end, construction with no drawing, and full-process transparency”. According to practices, the platform effectively improves the construction period, costs and project quality to lead the construction industry to digital transformation, and drive deep integration of the industrial chain.
4.
Yanyu WANG
Assistant Professor
Louisiana State University (US)
Title: Leveraging Artificial Intelligence for Predictive Analysis of Pavement Condition and Maintenance Costs in the Context of Natural Hazards
Abstract
Natural hazards significantly impact pavement integrity and maintenance costs, posing challenges to infrastructure management. This research integrates artificial intelligence (AI) to analyze a dataset of 100 records on natural hazards, pavement conditions, and maintenance costs. By utilizing machine learning models, the study aims to predict pavement deterioration and estimate future maintenance expenses, enabling proactive planning. The AI-driven models will consider factors like hazard type, pavement materials, traffic load, and maintenance history. The findings will provide infrastructure managers with insights to optimize maintenance strategies, reduce costs, and improve pavement resilience, advancing the role of AI in civil infrastructure management.
5.
Weiwei CHEN
Assistant Professor
University College London (UK)
Title: The Application of Digital Twins in Infrastructure
Abstract
The construction industry is currently grappling with issues such as low productivity, labour shortages, and frequent safety incidents. In this context, the applications of intelligent construction and digital twins have garnered widespread attention from both academia and industry. Digital twin technology, as one of the primary core technologies of intelligent construction, combined with sensing technology, AI and machine learning, holds promise in effectively addressing challenges such as 3D reconstruction, real-time monitoring, predictive maintenance and net zero carbon. This report primarily explores the application of digital twin technology in infrastructures, especially for construction and maintenance. Through real-world case studies, this report showcases the value and impact of digital twins in the realm of intelligent and sustainable construction.
6.
Fengjuan CHEN
Associate Professor
Beijing University of Technology (China)
Title: Building of A Digital Platform for Intelligent Construction of Civil Engineering Structures
Abstract
Construction of a digital platform for intelligent construction of civil engineering structures relies on physical models and mathematical algorithms. Utilizing BIM modeling, multidimensional digital modeling, and advanced simulation technologies, the built digital platform integrates big data and intelligent decision support systems. Notable applications include the design and case analysis of the Beijing Daxing International Airport, the Beijing Winter Olympics venues, and digital simulations for nuclear reactor decommissioning projects, demonstrating comprehensive capabilities in enhancing construction efficiency and safety through advanced digitalization.
7.
Jiaqi Li
Product Manager
Huaru Technology (China)
Title: Solution of Digital Twin Application in Supply Chain of Manufacturing Enterprises
Abstract
The digital upgrading of the supply chain is a megatrend and direction for enterprise development. This talk will present the current status and pain points of manufacturing enterprise supply chains, and combine a digital twin construction method to introduce the solution and technical route of “simulation+intelligent decision-making” empowering the digital transformation of enterprise supply chain.
