Project Overview

Bridge damage detection is crucial for ensuring the safety and integrity of the bridge structure. Traditional methods for damage detection often rely on manual inspections or sensor-based measurements, which can be time-consuming and costly. In recent years, computer vision techniques have shown promise in bridge displacement measurement and damage detection.

Aims

The overall aim of this project is to develop a cost-effective, easy-deployable monitoring system based on the computer vision, edge computing and advanced signal processing techniques, to effectively monitor bridges health.

Objectives

1. Develop computer vision-based structural displacement identification method. 
2. Develop reliable damage sensitive features from displacement responses.  
3. Develop a vision-based edge computing sensor node.

Significance 

The candidates will be working in the area of structural health monitoring of civil engineering structures on an Australian Research Council Early Career Industry Fellowship project titled, “Transforming Smart Bridge Monitoring by Computer Vision and Edge Computing.”  PhD candidates involved in this project will be trained under the supervision of Prof. Jun Li and Dr. Zhen Peng in the emerging field of smart infrastructure, which is critical for Australian society in the coming decade. This industry demand-driven project will offer PhD students exposure to industrial bridge maintenance practices and opportunities to address industry-identified challenges.

• Future Students

• Faculty of Science & Engineering
  • Science courses
  • Engineering courses

• Higher Degree by Research

• Australian Citizen
• Australian Permanent Resident
• New Zealand Citizen
• Permanent Humanitarian Visa
• International Student

• Merit Based

The annual scholarship package, covering both stipend and tuition fees, amounts to approximately $70,000 per year.

In 2024, the RTP stipend scholarship offers $35,000 per annum for a duration of up to three years. Exceptional progress and adherence to timelines may qualify students for a six-month completion scholarship.

Selection for these scholarships involves a competitive process, with shortlisted applicants notified of outcomes by November 2024.

1

All applicable HDR courses.

The successful candidate will need to demonstrate: 

1. Excellent written and verbal communication skills 
2. Strong computational, programming, algorithms, and data analysis skills 
3. Outstanding research skills 
4. Capacity to work independently and as a part of a team 

Applicants with the following knowledge and/or experience are highly preferred: 

1. Computer Vision, Vibration Signal Processing, Machine Learning/Deep Learning knowledge and/or; 
Experience Industry knowledge and/or; 
A track record of published (or at least submitted) research articles.

 

Please send your CV, academic transcripts and brief rationale why you want to join this research project via the HDR expression of interest form to the project lead researcher, listed below. 

You must be enrolled in a Higher Degree by Research Course at Curtin University by March 2025.

Project Lead: Dr Zhen Peng