Skip to main content

Ambient Vibration-Based Calibration of Finite Element Models of Bridges

Objective: The primary objective of this project is to develop a novel method for calibrating Finite Element models of bridges using ambient vibration data and drone imagery. This method will leverage the precision of IoT sensors or Laser Doppler Vibrometers (LDV) and the versatility of micro-drones to overcome traditional challenges in bridge modelling and inspection. Drone Survey and Data Collection: Micro-drones will be used to conduct a comprehensive survey of the bridge, capturing high-resolution images from various angles. These images will provide a detailed view of the bridge's current condition, including any visible structural weaknesses such as cracks or deformations. Ambient Vibration Data Collection: In addition to the drone survey, ambient vibration data will be collected using IoT sensors or Laser Doppler Vibrometers (LDV) placed strategically on the bridge. These devices will capture the vibration signals induced by ambient traffic, which will be used for calibrating the Finite Element model.Finite Element Model Calibration: The Finite Element model of the bridge will be developed to match modal parameters of the real structure based on the collected ambient vibration data. The stiffness, mass, and damping matrices of the model will be iteratively updated using a back-propagation algorithm until the dynamic response of the model matches the LDV measurement under the representative ambient traffic-induced vibration signal. This process is akin to how deep learning models are trained, making it a robust and efficient method for calibrating Finite Element models.Potential Impact: This project has the potential to revolutionize the field of bridge modelling and inspection. By making the process faster, more accurate, and more cost-effective, it could significantly improve the way we maintain and manage our existing infrastructure. Furthermore, the methods developed in this project could have broader applications in the field of structural engineering, potentially transforming the way we model and analyse all types of structures. Ideal Candidate: The ideal candidate for this project is a dedicated and innovative thinker with a strong background in Structural Engineering or related fields. They must have proficiency in Finite Element Modelling, and an interest in IoT, drone operations and image processing will be an advantage. The candidate should have strong analytical skills and the ability to work independently. They should also have a keen interest in bridging the gap between traditional structural engineering practices and emerging technologies. Experience with or willingness to learn machine learning methods is also desirable. 

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Michael Rustell - Michael is a chartered civil engineer (CEng MICE) who holds an Engineering Doctorate (EngD) in artificial intelligence in design automation of civil infrastructure. He has spent the past 8 years in industry working in ports and marine, oil & gas and nuclear industries and was a lead data scientist for the Europe, Middle East and Africa (EMEA) region at AECOM prior to joining Brunel.  Michaels interests include: deep learning, machine learning and data science in civil engineering, natural language processing, design automation and stochastic design methods.