STABILITY ASSESSMENT FOR SUSTAINABLE AND RESILIENT TUNNELLING USING AI

The demand for fast commuting between densely populated cities has increased over the last 30 years. The need of such infrastructures has risen along with the technological advancement and the environmental advantage to cars and social benefits that encounters. The latter implies that the number of railway and road tunnels connecting (remote) areas faster due to topographical limitations has also risen. One of the key considerations on existing tunnels especially in the UK is their stability state. More specifically, the Victorian tunnels that lie on the nation's railway network have been vital to facilitating a reliable rail service across the country for the past 150 years. However, some of these tunnels have started to challenge the existing network. These tunnels were originally constructed using several rings of brick masonry as a lining. After many years, these brick linings have started to degrade and bricks are becoming dislocated from their surrounding rings, falling on the tracks deteriorating the transport service. There is a gap of scientific and technical knowledge in the tunneling environment and conditions at which these tunnels were initially constructed which exacerbates their current stability assessment where in most cases these tunnels have exceeded their proposed lifetime (Atkinson et al 2020).
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 articial 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. Focusing on the long-term mechanisms (Paraskevopoulou, 2016; 2018, Paraskevopoulou et al. 2017, Paraskevopoulou and Diederichs, 2018) that have contributed to the current state aiming at expanding the current knowledge and practice performed by our industry partners, Bedi Consulting Ltd (BEDI), and National Rail 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 long-term tunnel performance. The latter will contribute to relating these to the geomechanical behaviour (Bedi et al 2017) of the surrounding rock masses proposing mitigation measures and design guidelines (Bedi and Orr 2014) for sustainable and resilient tunnelling projects.