Dr Ze Zhou Wang
- Marie Sklodowska-Curie Future Roads Fellow
Connect
Location
- Geotechnical and Environmental Engineering
About
Ze Zhou Wang is currently a Marie Skłodowska-Curie fellow at the University of Cambridge. Funded by the European Union and UK Research and Innovation, his research is at the intersection of infrastructure sensing, computational modelling, data science, artificial intelligence, reliability, geotechnical/infrastructure engineering. His current fellowship project at the University of Cambridge aims to utilize both historical performance data and continuous monitoring data to advance the understanding of how climate, microclimate, traffic, and natural hazards interactively impact England's road assets. He will then provide recommendations for maintenance and repair decisions that balance cost, carbon and environmental benefits.
Prior to Cambridge, Dr. Wang holds both a bachelor’s degree in civil engineering and a doctorate in geotechnical engineering from the National University of Singapore and was a visiting research scholar at the Massachusetts Institute of Technology. In his doctoral research, he developed and implemented a range of self-compatible computational techniques to streamline sensor data interpretation for construction safety control and decision-making. In his subsequent independent research, he pioneered the integration of deep-learning algorithms into geotechnical reliability analyses. In his other research projects, he developed innovative machine-learning algorithms to integrate geological features into the numerical reconstruction of geological subsurface stratigraphy. At present, results from his research led to 20 articles in leading international journals, such as ASCE Journal of Geotechnical and Geoenvironmental Engineering and Canadian Geotechnical Journal, 11 conference papers and two software. He was also the recipient of the best paper award from 8th International Symposium for Geotechnical Safety & Risk in Australia. Notably, the software he developed in his research is now being employed by government agencies for ongoing construction projects in Singapore
Fellowship period: 15 June 2023 – 24 April 2026
Research
Strategic Themes
Smart Materials
Research Project
Theme: Smart Materials
Abstract:
Pavement monitoring and health evaluation play a key role in highway operation and maintenance worldwide. As a result, extensive research has been devoted to improving pavement health evaluation methods. Determining the bulk material properties is one of the most critical steps in estimating a pavement’s remaining service life. Although various strategies exist to estimate these properties, non-destructive methods remain the most popular choice. The Traffic Speed Deflectometer (TSD) is an emerging non-destructive testing instrument for evaluating the in-situ stiffness of pavements. However, TSD does not directly measure the stiffness properties; instead, it measures the slopes of vertical deflection at a set of points along the right rear wheel of a measuring truck trailer travelling at normal traffic speed. Therefore, TSD measurement data needs to be interpreted to obtain the in-situ stiffness of pavements, and back analysis is commonly employed for this task.
A typical back analysis requires three components: (i) a calculation model to simulate pavement responses under the dynamic TSD loading, (ii) TSD measurements, and (iii) a back calculation algorithm. The calculation model generates simulated pavement vertical deflections with a given set of stiffness parameters, which are then compared against the actual TSD measurements. The back analysis algorithm then iteratively adjusts the input stiffness parameters until the generated vertical deflections reasonably match the actual measurements. At this point, the calibrated stiffness values are considered representative of the in-situ pavement properties. Many algorithms have been proposed for this task. However, existing algorithms are often based on deterministic analysis, thereby overlooking the inherent uncertainties in material properties. In this regard, this work pioneers Bayesian probabilistic back analysis of TSD measurements. Bayesian back analysis considers both the inherent uncertainties in material properties and uncertainties in TSD measurements, thereby providing more comprehensive information of in-situ stiffness properties for decision-making.
This work starts from a parametric analysis that investigates the effects of uncertainties in surface, base, and subgrade layer modulus. The results show that pavement deflection is most sensitive to uncertainty in subgrade layer modulus, followed by base layer modulus and surface layer modulus. This ranking implies that back analysis of TSD measurement is most effective in estimating subgrade layer modulus and least effective for surface layer modulus. The dominant sensitivity to subgrade layer modulus makes the simultaneous back analysis of surface, base, and subgrade layer modulus a practical challenge. Bayesian back analyses using simulated TSD measurements are then carried out to corroborate these results. In practice, it is recommended to obtain subgrade layer modulus through other means so that surface and base layer moduli can be more effectively interpreted using TSD measurements.
Project TRL: Start: TRL 3; End: TRL 6
Datasets: TSD measurement data
Dr. Wang’s research interest is at the intersection of infrastructure sensing, computational modelling, data science, artificial intelligence, reliability, and the broad domain of geotechnical engineering. He has several main research thrusts: (i) field monitoring and model updating of geosystems, (ii) geotechnical reliability analysis and design, and (iii) geotechnical site investigation and subsurface characterization. His research vision is to transform the assessment of geo/civil infrastructure over their entire lifespan through a synergistic paradigm that encompasses monitoring, interpretation, modelling, and assessment. This paradigm will collectively inspire the planning, analysis, design, operation, and intervention of next-generation infrastructure, addressing critical issues related to automation, adaptability, and resilience, among others. Ultimately, his paradigm will shift us from a data-driven to a knowledge-centric geo/civil infrastructure asset management, empowering both engineers and policymakers in building intelligent, adaptable, and sustainable cities of tomorrow.