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.<\/p>\n\n\n\n
Objectives<\/strong><\/h3>\n\n\n\n\n- 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.<\/li>\n\n\n\n
- To develop a physics-informed, data-driven method for identifying bridge modal vibration response from drive-by measurement<\/li>\n<\/ol>\n\n\n\n
Significance<\/strong><\/h3>\n\n\n\nThis 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.<\/p>\n\n\n\n
Ideal Candidate<\/strong><\/h3>\n\n\n\nThe candiate must be eligable to apply for Doctor of Philosophy – Civil Engineering.<\/p>\n\n\n\n
\n- Full time enrolment, for both domestic and international students<\/li>\n\n\n\n
- Minimum required: Bachelor degree (First class honours or upper second class honours) in Civil Engineering, Structural Engineering or related fields<\/li>\n\n\n\n
- Excellent written and verbal communication skills<\/li>\n\n\n\n
- Strong computational, programming, algorithms, and data analysis skills<\/li>\n\n\n\n
- Outstanding research skills<\/li>\n\n\n\n
- 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.<\/li>\n<\/ul>\n\n\n\n
This project is open to domestic and international applicants.<\/p>\n\n\n\n
Scholarship<\/strong><\/h3>\n\n\n\nThis 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.<\/p>\n\n\n\n
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.<\/p>\n\n\n \n\n \n\n \n \n ![\"Enquires](\"https:\/\/www.curtin.edu.au\/research\/wp-content\/uploads\/2026\/09\/Workign-together_-815x458-1.jpg.optimal-150x150.jpg\"\n)
\n\n \n \n
Significance<\/strong><\/h3>\n\n\n\nThis 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.<\/p>\n\n\n\n
Ideal Candidate<\/strong><\/h3>\n\n\n\nThe candiate must be eligable to apply for Doctor of Philosophy – Civil Engineering.<\/p>\n\n\n\n
\n- Full time enrolment, for both domestic and international students<\/li>\n\n\n\n
- Minimum required: Bachelor degree (First class honours or upper second class honours) in Civil Engineering, Structural Engineering or related fields<\/li>\n\n\n\n
- Excellent written and verbal communication skills<\/li>\n\n\n\n
- Strong computational, programming, algorithms, and data analysis skills<\/li>\n\n\n\n
- Outstanding research skills<\/li>\n\n\n\n
- 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.<\/li>\n<\/ul>\n\n\n\n
This project is open to domestic and international applicants.<\/p>\n\n\n\n
Scholarship<\/strong><\/h3>\n\n\n\nThis 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.<\/p>\n\n\n\n
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.<\/p>\n\n\n \n\n \n\n \n \n \n\n \n \n
The candiate must be eligable to apply for Doctor of Philosophy – Civil Engineering.<\/p>\n\n\n\n
- \n
- Full time enrolment, for both domestic and international students<\/li>\n\n\n\n
- Minimum required: Bachelor degree (First class honours or upper second class honours) in Civil Engineering, Structural Engineering or related fields<\/li>\n\n\n\n
- Excellent written and verbal communication skills<\/li>\n\n\n\n
- Strong computational, programming, algorithms, and data analysis skills<\/li>\n\n\n\n
- Outstanding research skills<\/li>\n\n\n\n
- 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.<\/li>\n<\/ul>\n\n\n\n
This project is open to domestic and international applicants.<\/p>\n\n\n\n
Scholarship<\/strong><\/h3>\n\n\n\n
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.<\/p>\n\n\n\n
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.<\/p>\n\n\n
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