Civil infrastructure including bridges, buildings and other structures are crucial for a society to function. Optimal utilisation, maintenance and management of existing civil infrastructure have been identified as a critical factor in ensuring and raising economic productivity.
Aim
The primary aim of this project is to develop advanced generative and physics-informed Artificial Intelligence (AI) techniques, for effective Structural Health Monitoring (SHM) and condition assessment of civil engineering structures, by enhancing the AI capacity with synthetic data and physics domain knowledge.
Objectives
- Developing generative AI techniques to identify structural monitoring data anomalies, lost data recovery and response reconstruction.
- Developing physics-informed and generative AI techniques for structural condition monitoring.
- Conducting comprehensive validations of the developed approaches on experimental structures and applications to large-scale structures.
Significance
This project can be used to reliably monitor the performance and condition of civil infrastructure with limited monitoring data, especially under extreme events, and strongly supports condition-based management and maintenance strategy of civil engineering structures to reduce operation interruption and maintenance cost.
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.
Scholarship
This scholarship supports students working on an Australian Research Council (ARC) Discovery Project focused on structural health monitoring of civil engineering infrastructure. The project aims to develop advanced generative and physics-informed artificial intelligence techniques to improve structural condition monitoring, particularly under limited monitoring data. Scholarship recipients will contribute to innovative research that enhances infrastructure safety, resilience, and maintenance efficiency, while gaining strong training in AI-driven civil engineering and data-informed decision-making.
Two scholarship are available that include a living stipend of $38440 p.a. pro rata indexed, based on full-time studies, for up to a maximum of 3.5 years.
Applications close 30 May 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.