Structural health monitoring by using generative and physics-informed AI

26 Mar 2026 | Laura Perks

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 crowd sourced 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

The candiate must be eligable to apply for Doctor of Philosophy – Civil Engineering.

Full time enrolment, for both domestic and international students
Minimum required: Bachelor degree (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 experience 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 and international applicants.

Scholarship

This scholarship is funded by the 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 scholarship are available that provide a three years stipend ($38,440 stipend per annum, based on full-time studies), with half a year potential extension. Tuition fees offset support is also available for a successful international candidate.