Dr Fengqiao Zhang
- Marie Sklodowska-Curie Future Roads Fellow
- Assistant Professor in Concrete Structures at TU Delft, Netherlands
Connect
Location
- Digital Roads of the Future - Smart materials
About
Dr. Fengqiao Zhang is a Maria Skłodowska-Curie Future Roads Fellow at the University of Cambridge (UOC). She received her PhD degree in Civil Engineering at Delft University of Technology (the Netherlands) in 2022. Before joining the UOC, he worked as a Postdoctoral Fellow at Delft University of Technology (the Netherlands) from 2022 to 2023. She has participated the project funded by Conference of European Directors of Roads (CEDR) as a co-lead, working on evaluation various monitoring technologies for road health monitoring. Dr Zhang has written well-recognized publications regarding acoustic emission monitoring, concrete crack kinematics, concrete shear failure assessment, probabilistic analysis of monitoring data. She also serves as a reviewer for high-impact international journals. Dr Zhang was a recipient of Honourable Student Paper Awards (2022) and excellent full scholarship from Delft Research Initiatives Mobility and Infrastructures (DIMI) (2022).
Fellowship period: 3 January 2024 – 31 July 2024
Research
Research Interests
Dr. Fengqiao Zhang’s research interests include structural health monitoring, data integration, probabilistic analysis, artificial intelligence, structural assessment.
Strategic Themes
Smart Materials
Research Project
Title: An integrated road asset monitoring system supported by probabilistic models and artificial intelligence
Theme: Smart Materials
Abstract:
Effective utilization of information is critical for road asset management. National Highways (NH) highlights the importance of data-driven proactive asset management. Despite the advancement of technologies like embedded sensors, smart cars, and Artificial Intelligence (AI) for data interpretation, NH has not fully capitalized on these innovations. Data from diverse sources often remain underutilized, indicating a lack of efficient integration tools for comprehensive understanding and decision-making in road asset management, both reactively and proactively.
This research aims to examine emerging technologies, integrating their data to improve our understanding of road asset performance, now and in the future. A shift from deterministic to probabilistic analysis is important. Given that every technology encompasses uncertainties that cannot be fully eliminated, it's essential to quantify these uncertainties and incorporate the data alongside its uncertainties. This integration can be accomplished via various probabilistic models such as Bayesian updating, as well as AI methods such as ensemble learning.
Moreover, as technologies continuously evolve, there will be a persistent necessity to identify, comprehend, and incorporate new advancements. Accordingly, this research will also establish a structured and future-proof system to integrate technologies dynamically for road monitoring. The system remains relevant, adaptable, and updated in the face of future developments. The proposed system will undergo validation through both laboratory and on-site experiments. Furthermore, we will provide a comprehensive user manual to facilitate NH in effectively implementing the proposed changes, both in the present and in the future.