On the afternoon of November 5th, the research group held the 12th session of “Li Nan Research Group’s Academic Lecture Series”. The event was presided over by Dr. Li Nan, and Dr. Lu Qiuchen from University College London was invited to give a keynote report on “Digital Twin in the Built Environment: Principles and Implementation”. The report was attended online by more than 50 teachers and students from such universities as Tsinghua University, Shanghai Jiao Tong University, Nanjing University of Aeronautics and Astronautics, Chang’an University, and Shenzhen University.
Dr. Lu Qiuchen delivered a keynote report on how to create digital twin in built environment and apply digital twin at the levels of buildings and cities. The report was divided into three parts. The first part described how to use CAD drawings and architectural image data to efficiently create a geometrical digital twin model. The second part discussed the multi-level architecture and data format of digital twin, and presented the case study of a multi-level digital twin in the West Cambridge area of the University of Cambridge. The third part mainly introduced the application of digital twin combined with other technologies in building systems and medical systems.
The first part of the report described how to efficiently create a geometrical digital twin model. Specifically, first, CAD drawings and building image data of different building components are collected. Object recognition heuristic algorithm and neural network method are used to identify components such as beams, slabs and columns in CAD drawings. Later, building image data is used to complete the location information of components. Finally, a geometrical digital twin model is created based on the IFC standard and CAD drawings and building image data.
Figure 1 Digital Twin creation
The second part of the report took the West Cambridge campus of University of Cambridge as an example to introduce the multi-level structure, data format creation and the specific implementation of the digital twin. Dr. Lu Qiuchen’s team proposed the multi-level architecture of digital twin, namely, data acquisition level, data transmission level, data modeling level, data fusion level and application level. This multi-level architecture is suitable for the creation of digital twin at the levels of building subsystems, single buildings, and cities. Subsequently, the report also described how to create digital twin data structure by integrating multi-source heterogeneous data in the IFC format to enable efficient data query and analysis. Finally, Dr. Lu also detailed the process of creating multi-level digital twin in the West Cambridge campus of University of Cambridge. Specifically, a high precision (LoD 4 / 5) machine room’s BIM model is created at the level of building sub-system, medium precision (LoD 2 / 3) manufacturing institute’s BIM model created at the level of single building, and at the city level, the data collected with drones and vehicle-mounted laser scanner are used to generate point cloud model, grid model and object-oriented geometrical digital twin model successively. Dr. Lu’s team not only independently developed a digital twin research platform, but also cooperated with Bentley Company to develop a digital twin commercial platform. The platform can be used for building component failure monitoring, energy consumption simulation, urban traffic prediction, infrastructure operation and maintenance optimization, etc.
Figure 2 Digital Twin data format
Figure 3 Example of a multi-level digital twin realized in the West Cambridge campus of University of Cambridge
The third part of the report introduced the applications of digital twin technology combined with other technologies. Dr. Lu Qiuchen introduced how to integrate digital twin and virtual reality technology, and use machine learning methods to monitor the failure of components in the operation and maintenance stage of buildings. In addition, Dr. Lu also took the British medical system as an example to introduce how to combine digital twin with blockchain technology during the COVID-19 pandemic to help the medical system cope with the shortage of medical resources and optimize the allocation of limited medical resources among different hospitals.
Figure 4 Optimizing medical resource allocation based on digital twin and blockchain technologies
After the report, Dr. Lu Qiuchen exchanged with the teachers and students who attended the lecture online. Teachers had in-depth discussions with Dr. Lu Qiuchen on how to use digital twin in different disciplines, how to summarize the scientific issues and innovation points of the applied fund projects depending on digital twin technology, and how to reduce the digitalization cost of long-span infrastructure such as high-speed rail and road network. Meanwhile, the students shared their opinions and questions on how to collect and integrate large-scale urban data and how to promote the market application of digital twin technology. Dr. Lu Qiuchen answered the questions one by one and had an active discussion with the attendees. Finally, Dr. Lu encouraged the students to pay attention to the market demand during their doctoral study, and use the market demand to guide and improve their own research. In the future, they should learn to establish the connections between different parts of the research and develop their own research system.
