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Bridging the Gap: Integrating Neural Radiance Fields and Micro-drones for Enhanced 3D Volumetric Finite Element Analysis

Objective: The central objective of this project is the innovative deployment of Neural Radiance Fields (NeRFs) and micro-drones in the process of 3D volumetric Finite Element modelling of bridges. This approach seeks to address and alleviate traditional difficulties encountered in bridge modelling and inspection NeRFs Overview: NeRFs, stemming from the domains of machine learning and computer vision, present an exceptional technique for synthesising new perspectives of intricate 3D scenes from a limited set of initial images. The method involves training a deep neural network to convert 3D coordinates into a colour and opacity, thereby learning a continuous function of the volumetric scene. This function can subsequently be sampled from new viewpoints to produce further images of the scene.Application of NeRFs to 3D Volumetric Mesh Element Model Generation: In this project, we propose using NeRFs for the generation of 3D volumetric mesh element models of bridges. Micro-drones capture a series of images from various vantage points, which serve as the data input. These images are then used to train a NeRF, resulting in a continuous 3D function representing the bridge. This function can be sampled to create a detailed 3D volumetric mesh of the bridge, which is a crucial prerequisite for any finite element analysis.Existing Bridge Modelling Challenges: Developing accurate models of existing bridges is a multifaceted challenge, as it necessitates extensive data on the bridge's geometry and material properties. Obtaining such data can be a complex and resource-intensive task, particularly for large or intricate structures. Traditional data collection methods like manual inspection or laser scanning often involve significant costs and can fail to capture all essential details. Transforming this data into a Finite Element model further requires specialised knowledge and software.Micro-drones: The utilisation of micro-drones offers a novel solution to the aforementioned data collection challenges, by accessing difficult-to-reach bridge sections and capturing high-resolution images from diverse perspectives. This approach can significantly decrease the cost and time required for data collection, while simultaneously enhancing the quality and comprehensiveness of the data.Addressing the Issue: This project presents a novel strategy for bridge modelling by combining the strengths of micro-drones and NeRFs. The intention is to develop a methodology for creating accurate 3D volumetric finite element models of bridges that is faster, more detailed, and more cost-effective than conventional approaches. Drones will capture the required data, and the NeRFs will process this data into models. This opens up new possibilities for bridge inspection and maintenance.Potential Impact: The proposed project has far-reaching implications. By improving the efficiency, accuracy, and cost-effectiveness of bridge modelling and inspection, it could significantly enhance the way we manage and repair our infrastructure. The methods developed could also be applied more broadly within structural engineering, potentially reshaping the way we model and analyse various structures.Ideal Candidate: The successful candidate for this project will be an innovative thinker with a robust background in Structural Engineering or related fields. Proficiency in Finite Element Modelling is essential, and familiarity with IoT, drone operations and image processing would be advantageous. The candidate should possess strong analytical skills, the capacity to work autonomously, and have a keen interest in connecting traditional engineering practices with emerging technologies. Experience or a willingness to learn machine learning methods would also be 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.