The UK rail network, operated and maintained by Network Rail, has over 900 operational tunnels, along 400km of routes, with an average age of approximately 150 years. Given the sheer number of tunnels on the Network and their age, rapid and accurate condition assessment of all these old tunnels is a safety-critical task for Network Rail. Trains operate at speeds in excess of 100mph through these tunnels, and so debris on the tracks presents a real risk of derailment, as defecting tunnel linings into the running clearance of the trains.
Currently, condition assessment of all Network Rail tunnels is conducted by assessors through manual, visual, and tactile survey from track level and where necessary through the use of scaffolds or Mobile Elevated Working Platforms. Due to the health and safety risks associated with working on the railway, the manual and subjective assessment process as well as disruptive access that includes line blocks in which trains are not allowed to run, which leads to disruption to the travelling public, there is a concerted effort to automate the condition assessment of these tunnels.
Another main limitation of the current method of condition assessment is that examination reports for tunnels are produced using manual entry of visually logged defects in to a Microsoft Excel spreadsheet that provides a schematic record of the examination findings and a Tunnel Condition Marking Index that scores the tunnel condition. The visual nature of examinations and the reliance on qualitative and often subjective records can lead to miscommunication when comparing the records of examinations carried out at different times or by different assessors. This subjectivity in assessment poses a safety concern in that defects are often missed due to the didcult environment and conditions within the tunnel under which qualitative assessments are visually made.
The main aim of this research project is to investigate the applicability of artificial intelligence (AI), machine learning/deep learning and signal/image processing technologies to rapidly recognise common defects that are observed in tunnels due to various geological and geomechanical phenomena that can take place over the lifetime of these Victorian tunnels. The research project will expand on work already done by our industry partner, Bedi Consulting Ltd (BEDI), and from data captured using emerging technologies such as 3D laser scanning, photogrammetry and drone based survey to investigate the application of automation/learning algorithms to objectively and quickly recognise and characterise common tunnel lining defects and relate them to special geological cross-sections investigating the specific failure mechanisms. If successful, this product of this research may be applied directly to tunnel condition assessment, thereby minimising operational impact on the already crowded rail network, and ultimately improve safety, reliability and efficiency in tunnel condition assessment process, securing also the national heritage.
Applicants should have a BSc degree (or equivalent) in geology, earth sciences, geophysics, civil/mining engineering or a similar discipline. An MSc in engineering geology, geological/geotechnical engineering or applied geoscience (or similar) is desirable. Skills in field-based geological data collection and field sedimentology and stratigraphy are desirable. Experience of using numerical analyses software would be useful, though is not essential.
This PhD will commence 1st October 2021 and run for 3.5 years. During this period the student will be eligible for all the postgraduate training typically provided to students attending the University as part of the NERC Doctoral Training Programme. The student will receive thorough training in the critical appraisal of subsurface data, experimental rock mechanics and finite-element based numerical modelling. The latter will be gained via extended visits to our case partner, Bedi Consulting Ltd., the project partner. This multi-disciplinary training will place the successful student in an ideal position to work in a range of industries or take up an academic appointment.