Dr Zhaojie Sun
- Marie Sklodowska-Curie Future Roads Fellow
Connect
Location
- Digital Roads of the Future - Data Science
About
Dr. Zhaojie Sun received his Bachelor degree in Civil Engineering from Harbin Institute of Technology (HIT) in 2012. After that, he continued his study in HIT and got his Master degree in Transportation Engineering in 2014. Then, he obtained his PhD degree in Pavement Engineering from Delft University of Technology in 2022 and worked as a Postdoc Researcher in the same section for about two years. Since 2024, he joined the Department of Engineering at the University of Cambridge as a Marie Skłodowska-Curie Future Roads Fellow.
Dr. Zhaojie Sun has the expertise of pavement structure dynamic analysis, pavement structural quality evaluation, tire-pavement interaction, and pavement material performance evaluation.
Fellowship Period: 2 January 2024 – 1 January 2027
Research
Strategic Themes
Data Science
Research Project
Theme: Data Science
Abstract:
Road infrastructure plays a critical role in facilitating mobility, supporting economic activities, and promoting social development. Hence, it is important to maintain the high performance of roads by conducting timely maintenance and rehabilitation activities. In order to formulate accurate maintenance and rehabilitation strategies, a digital twin of roads can be used to identify the latest health condition of roads in service. In this process, an essential step is to obtain the structural health information of existing roads, which will be addressed in this study. At first, a physics-based model which can simulate a Non-Destructive Testing (NDT) method of roads will be developed by using a semi-analytical approach. Then, the developed model will be used to generate a database which contains the structural parameters and corresponding predicted NDT results of roads. At last, the generated database will be used to develop a data science-based technique which can identify structural parameters of roads in service based on NDT results, and the identified structural parameters will be used to evaluate the structural health and predict the remaining life of existing roads. The digital twin of roads with the latest structural health information can help formulate the most suitable maintenance and rehabilitation strategies, which ensure the good service performance of roads.
The objective of this study aligns with the Sustainable Development Goals by making road infrastructure resilient to traffic loads and environmental factors (Goal 9), promoting economic growth due to enhanced mobility (Goal 8), and improving people’s quality of life (Goal 3).
The starting Technology Readiness Level of this study is Level 3, which corresponds to the understanding of the NDT method from theoretical perspectives. The expected end Technology Readiness Level is Level 8, which indicates the integration of the developed technique into the commercial design of the NDT device.
The NDT measurements from the TRL company can be used to validate the accuracy of the developed technique. To assess the compatibility of the developed technique with existing pavement management systems, a secondment at National Highways could be arranged.
Dr. Zhaojie Sun’s research interests include data science techniques, pavement dynamics, and structural health monitoring of pavements.