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Automating inspection of railway earthworks (Ref: RAINDROP-AB2)

Loughborough University School of Architecture, Building and Civil Engineering
Self-funded ⏰ Closing Soon 🎓 Civil Engineering 🎓 Geotechnical Engineering 🎓 Materials Science 🎓 Mechanical Engineering 🎓 Mechanics 🎓 Structural Engineering 🎓 Systems Engineering mechanical engineering robotics systems engineering civil engineering engineering geotechnical engineering materials science mechanics mechatronics structural engineering

This PhD project develops computer vision techniques to automate inspection of railway earthworks using imagery from asset inspections and drones, detecting defects such as cracking, erosion, and drainage problems. Integrated with LiDAR and track geometry data, the tools will enhance safety, resilience, and consistency in infrastructure monitoring.

Project Description

Deterioration of earthworks slopes (cuttings and embankments), which support transport infrastructure and act as flood defences, is accelerating under increasing weather extremes resulting from global change, damaging critical infrastructure resilience. This project is part of the prestigious Loughborough University Vice Chancellor’s PhD Cluster – RAINDROP (Resilient eArthwork INfrastructure: Diagnosis, RehabilitatiOn & Prognosis).The Doctoral Researcher (DR) will join a cohort of DRs working on a series of interlinked, interdisciplinary projects for sustainable, intelligent, and climate change-resilient infrastructure slopes. This PhD will develop computer vision techniques to enhance visual inspection of railway earthworks. The project will use imagery from asset inspections and drones to automatically detect defects such as cracking, erosion, and drainage problems, and integrate these with LiDAR and track geometry data to provide objective indicators of condition and deterioration. The research will deliver automated inspection tools that improve consistency, enable earlier detection of instability, and enhance the safety and resilience of railway infrastructure. Feel free to reach out to the primary supervisor, a.blanco-alvarez@lboro.ac.uk, if you have any questions.

Entry Requirements

Applicants should hold or expect to achieve a first-class or high 2:1 honours degree (or international equivalent) in Civil Engineering, Geotechnical Engineering, Computer Science, Electrical/Electronic Engineering, Geomatics, Applied Mathematics, or a closely related discipline with a strong quantitative focus. Applicants must demonstrate solid foundations in computer vision and image processing, machine learning or statistical modelling, and programming in Python, MATLAB, or a comparable language. Evidence of experience in quantitative data analysis and handling complex engineering datasets is essential.

Desirable Skills:
- A relevant master’s degree or equivalent industrial experience in infrastructure monitoring, computer vision, or data-driven engineering applications will be advantageous.
- Experience with deep learning frameworks, LiDAR data processing and point cloud analysis, UAV/drone-based data acquisition, multi-sensor data fusion, or condition monitoring of civil infrastructure is desirable.
- Prior exposure to geotechnical engineering, infrastructure asset management, or railway systems would be beneficial but is not essential.

English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).

How to Apply

All applications should be made online. Under Campus, please select ‘Loughborough’ and select Programme 'Architecture, Building and Civil Engineering'. Please quote the advert reference ‘RAINDROP-AB2’ in your application. All applications must include a completed studentship application form (instead of a personal statement), a two-page research proposal based on the project description outlining how you would approach the project, and an up-to-date CV. A personal statement is not required.

Only applicants with the minimum supporting documents, comprising a CV, two-page research proposal, and studentship application form will be considered for an interview. Please ensure you have uploaded all these documents in addition to certificates and transcripts from all previous undergraduate and postgraduate qualifications and evidence of how you meet the university's English Language requirements (if available at the time of application).

Eligibility

UK/Home
EU
International

Supervisor Profile

DA
Dr Ana Blanco
Loughborough University, School of Architecture, Building and Civil Engineering

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