It is highlighted that about 70% of Australia’s bridges are more than 50 years old and rapidly approaching end of service life. Ageing bridges pose an urgent and significant challenge to asset owners for maintenance, owing to structural damage accumulated over years of use, increasing traffic loads, and exposure to harsh environmental conditions.
Aim
The overall aim of this project is to develop a novel framework for city-scale bridge safety monitoring using drive-by sensing data collected from crowdsourced moving vehicles.
Objectives
- To develop a scalable mobile crowdsensing data collection platform that incorporates a practical drive-by data processing pipeline, specifically designed to enhance the quality and reliability of drive-by sensing data.
- To develop a physics-informed, data-driven method for identifying bridge modal vibration response from drive-by measurement.
Significance
This proposed project will enable asset owners to make data-informed decisions in managing ageing bridge infrastructure and prioritizing limited maintenance funding towards the bridges most in need.
Ideal Candidate
- Full time enrolment
- Minimum required: Bachelor degree (the first class honours or upper second class honours) in Civil Engineering, Structural Engineering or related fields
- Excellent written and verbal communication skills
- Strong computational, programming, algorithms, and data analysis skills
- Outstanding research skills
- Applicants with Master degrees by research with technical publications and research experiences in structural dynamics and structural health monitoring, especially on machine learning, deep learning, signal processing and data analysis techniques, are preferred.
This project is open to domestic students.
Scholarship
This scholarship supports students undertaking research under the support of an Australian Research Council (ARC) Discovery Project on city-level structural health monitoring of bridges using drive-by sensing. The project integrates advanced signal processing and physics-informed optimisation techniques to identify bridge modal parameters from vehicle-based measurements. Scholarship recipients will contribute to the development of scalable and cost-effective bridge monitoring frameworks, supporting improved infrastructure safety, reduced maintenance costs, and enhanced resilience of urban transport networks.
Two Scholarships are available that provide initially three years stipend (~ $38,440 stipend per annum, based on full-time studies), with half a year potential extension.
Applications close May 30th 2026
Enquiries
For enquires please contact Professor Jun Li at junli@curtin.edu.au
To apply please submit an Expression of Interest to Prof Jun Li.