Dr Junxiang Zhu
- Marie Sklodowska-Curie Future Roads Fellow
- Cohort 3 Fellows representative
About
Before joining the Department of Engineering at the University of Cambridge as Marie Sklodowska-Curie Future Roads Fellow, Junxiang worked at Curtin University for 5 years as research assistant, research fellow, lecturer, and senior lecturer, where he managed and delivered courses relating to digital construction (such as BLDG2015 Building Information Modelling and Management and BLDG3016 Construction Planning and Scheduling) and involved in the development of the new Virtual Design and Construction course. He has won a teaching excellence award at Curtin and is an associate fellow of Higher Education Academy (AFHEA). He completed his PhD in Construction Management at Curtin in 2019 on the topic of integration of Building Information Modelling (BIM) and Geographic Information System (GIS) for smart infrastructure management. He managed to develop a comprehensive framework for BIM/GIS integration and effective methods for using BIM information in GIS, which is fundamental to digital twinning. His recent work includes the development of a graphical representation of BIM information which supports efficient query of interconnected asset information. By far, he has authored/co-authored more than 20 papers published in peer-reviewed journals, such as Automation in Construction, and three of his papers were recognised as highly cited papers and hot papers.
Fellowship period: 03 January 2024 – 02 January 2027
Research
Strategic Themes
Digital Twin
Research Project
Title: A graph-based approach for road digital twins
Theme: Digital Twin
Abstract:
The major problem to be addressed in the project is how to design road digital twins. This problem will be addressed by achieving the following four objectives: (1) Objective 1: Identifying the specific information requirements for road digital twin (2) objective-2: Developing a graph-based reference data library for road assets; (3) objective-3: Developing an extensible, future-proof, and scalable road integration cloud architecture; and (4) objective-4: Validating the proposed approach for road digital twinning. These objectives are identified and scheduled logically, and, to achieve these objectives, the standard procedure specified in an international standard, ISO19650 organization and digitalization of information about buildings and civil engineering works, will be referenced. To be specific, the following methods will be used: (1) Objective 1: Relevant literature will be reviewed and interviews/questionnaires with stakeholders, such as Jacobs and National Highways, will be conducted to learn the specific information requirements for future road management and define the road Foundation Data Model. (2) Objective-2: Graph-based technologies, such as Resource Description Framework (RDF) and labelled property graph (LPG), will be compared to select the proper graph technology for implementing the graph reference data library that is developed based on information requirements identified in Objective-1. (3) Objective 3: An extensible, future-proof, and scalable road integration cloud architecture will be developed by working with stakeholders, taking data fusion and data interoperability into consideration. (4) Objective 4: Real project data will be used to validate the effectiveness of the proposed approach for road digital twinning.
Alignment with SDG:
- SDG 9 Industry, Innovation, and Infrastructure,
- SDG 11 Sustainable Cities and Communities, and
- SDG 12 Responsible Consumption and Production.
Project TRL: the starting TRL (technology readiness level) of graph-centric DT is ‘1 Basic Principles Observed’, and the expected end TRL is ‘5 Technology Validated In Relevant Environment’.
Industry secondment needs: I need to do an industry secondment to learn the real practice of road maintenance, in order to create a data model for the road section, which can be used with property graph.
Datasets: This project has received two 3D road models in IFC4.3 from Trimble. The initial test shows that IFC road models mainly contains geometric information (> 98%), while the semantic information is less than 2%, which shows the need to enrich the semantics of road data models.
Junxiang’s research interests include City Information Modelling (CIM), Building Information Modelling and Management (BIM), Geographic Information System (GIS), information fusion/integration, and digital construction.