STABILITY ASSESSMENT FOR SUSTAINABLE AND RESILIENT TUNNELLING USING AI

在过去的30年里，人口密集的城市之间对快速通勤的需求有所增加。这种基础设施的需求沿着着技术的进步和对汽车的环保优势以及所遇到的社会效益而上升。后者意味着，由于地形限制，连接（偏远）地区的铁路和公路隧道数量也有所增加。对现有隧道的关键考虑因素之一，特别是在英国，是它们的稳定状态。更具体地说，在过去的150年里，位于全国铁路网上的维多利亚隧道对促进全国可靠的铁路服务至关重要。然而，其中一些隧道已经开始挑战现有的网络。这些隧道最初是用几圈砖砌成的。多年后，这些砖衬开始退化，砖块从周围的环上脱落，落在轨道上，破坏了运输服务。在这些隧道最初建造的隧道环境和条件方面存在科学和技术知识的差距，这加剧了其当前的稳定性评估，在大多数情况下，这些隧道已经超过了其拟定的使用寿命（Atkinson等人，2020年）。目前，所有网络铁路隧道的状况评估都是由评估人员通过人工、视觉和触觉调查从轨道水平进行的，必要时还可以使用脚手架或移动的高架工作平台。由于与铁路工作相关的健康和安全风险，人工和主观评估过程以及中断性访问，包括不允许列车运行的线路块，这导致对乘客的干扰，因此需要共同努力，使这些隧道的状况评估自动化。当前状态评估方法的另一个主要局限性是，隧道检查报告是通过手动将视觉记录的缺陷输入Microsoft Excel电子表格中生成的，该电子表格提供了检查结果的示意性记录和对隧道状态进行评分的隧道状态标记指数。检查的直观性以及对定性和往往主观的记录的依赖，在比较不同时间或不同评估员进行的检查记录时，可能导致沟通错误。这种主观性的评估造成了安全问题，因为缺陷往往被错过，由于didcult环境和条件下，在隧道内进行定性评估的视觉。该研究项目的主要目的是调查人工智能（AI），机器学习/深度学习和信号/图像处理技术的适用性，以快速识别由于在这些维多利亚时代隧道的寿命期间可能发生的各种地质和地质力学现象而在隧道中观察到的常见缺陷。注重长期机制（Paraskevopoulou，2016年; 2018年，Paraskevopoulou等人，2017年，Paraskevopoulou和Diederichs，2018年），这些都有助于目前的状态，旨在扩大我们的行业合作伙伴Bedi Consulting Ltd（BEDI）和国家铁路公司从使用3D激光扫描等新兴技术捕获的数据中所进行的现有知识和实践，摄影测量和基于无人机的调查，研究自动化/学习算法的应用，以客观快速地识别隧道的长期性能。后者将有助于将这些与周围岩体的地质力学行为（Bedi等人，2017年）联系起来，为可持续和弹性隧道工程项目提出缓解措施和设计指南（Bedi和Orr，2014年）。

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.