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Autonomous Drone Surveys and Convolutional Neural Networks for Bridge Maintenance: A Predictive Approach Using Finite Element Analysis

Objective: The primary objective of this research is to develop a comprehensive framework for bridge maintenance that integrates autonomous drone surveys, convolutional neural networks (CNNs), and Finite Element analysis. This approach aims to identify structural weaknesses in bridges, particularly cracks, and use this information to update a Finite Element model of the bridge. The updated model can then be used to predict when intervention is required, shifting the paradigm from reactive to condition-based maintenance.Background on Bridge Maintenance: Maintaining the structural integrity of bridges is a complex task that requires detailed and accurate data on the bridge's condition. Traditional methods of data collection, such as manual inspection, can be time-consuming, costly, and may not capture all the necessary details. Furthermore, many bridges are ideal candidates for creating 3D meshes from surface meshes as they typically do not have significant internal voids, unlike buildings.Use of Autonomous Drones: Autonomous drones can help overcome many of the challenges associated with data collection for bridge maintenance. They can easily access hard-to-reach areas of the bridge, capturing high-resolution video footage from a variety of angles. This footage can then be analyzed using convolutional neural networks to identify cracks and their severity.Convolutional Neural Networks for Crack Identification: Convolutional neural networks (CNNs) are a type of deep learning algorithm that can analyze visual imagery. In this research, a CNN will be trained to identify cracks in the drone footage, providing detailed information about their location and severity. This data will then be used to update the finite element model of the bridge.Finite Element Analysis for Predictive Maintenance: Finite Element analysis is a numerical method used for predicting how a structure will react to certain forces, vibration, heat, and other physical effects. In this research, a Finite Element model of the bridge will be updated based on the data from the CNN. This updated model can then be used to run analyses and predict when and where intervention is required, enabling a shift towards condition-based maintenance.Potential Impact: This research has the potential to revolutionize the field of bridge maintenance. By integrating autonomous drone surveys, convolutional neural networks, and Finite Element analysis, it could significantly improve the accuracy and efficiency of bridge inspections, and enable more effective maintenance strategies. Furthermore, the methods developed in this research could have broader applications in the field of civil engineering, potentially transforming the way we maintain and repair 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.