PSI - Issue 64

Jack Smith et al. / Procedia Structural Integrity 64 (2024) 220–227 Smith, J., Paraskevopoulou, C. Structural Integrity Procedia 00 (2019) 000 – 000

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1. Introduction It is vital that we efficiently operate and maintain our underground infrastructure to ensure it is sustainable for future generations (Paraskevopoulou et al., 2019). Railway tunnels form a key part of our transportation connectivity and a significant proportion of the world’s underground infrastructure . However, in many countries the majority of these tunnels are masonry lined and were constructed over 150 years ago. The condition of these tunnels must be regularly assessed to inform maintenance activities and ensure safety. Condition assessments generally involve an in person structural inspection where an assessing engineer manually annotates the type, severity and location of structural damage onto a plan of the structure. To avoid disruptive railway closures, these mostly take place overnight. Accurate and regular assessments help asset managers to understand how much structural rehabilitation is required (Atkinson et al., 2021), reducing the need for costly (Paraskevopoulou et al., 2021; 2022) and carbon intensive new constructions. Historic masonry tunnels typically feature large areas of low severity spalling and efflorescence damage, which is time-consuming to document and can obscure other damages (Chiu et al., 2015). This makes them challenging to accurately inspect within the time constraints of an overnight closure. As a result, inspections incur a substantial labour cost and their outputs can be subjective to the assessing engineer’s judgement . With the global push for net zero and increasingly tight operational budgets, there is mounting pressure for the condition assessments to be more repeatable and cost-effective. Digitalisation of the assessment process can help achieve these aims. There has been a considerable recent effort to automate railway infrastructure condition assessment tasks. While most research has focused on using photographic data or specialist sensors such as GPR and Ultrasound for digital condition assessments (Llanca et al., 2017), terrestrial lidar scanning (TLS) offers significant potential. TLS is increasingly routinely undertaken during structural inspections for documentation and measurement purposes, so further utilizing this data would require no additional equipment or change to established inspection procedures. Research has shown that using tunnel lining 3D point clouds obtained by lidar to directly identify tunnel lining damage can be significantly more efficient than manual methods (Kaartinen et al., 2022). Further to this, fully automated tunnel assessment procedures have been proposed that use recent advances in computer vision using machine learning to identify and locate damage locations on these point clouds (Zhou et al., 2021; Smith et al., 2023b; Bahreini and Hammad, 2024) . Many of these tackle damage localization as a pixelwise semantic segmentation problem. This involves classifying individual pixels in images of a structure as undamaged or damaged. Nevertheless, most of these methods have focused on concrete lined tunnels, with only a few studies looking at damage on masonry infrastructure. Masonry damage segmentation has the additional combined challenges of non homogeneous surfaces, the frequent existence of multiple overlapping defect types and a variety of masonry joint geometries (Chiu et al., 2015). Examples of these are shown in Fig. 1. Certain studies have created multi step automated workflows to combat these challenges, such as Smith et al. (2023b) who combined a geometric block face plane fitting strategy with deep learning-based block identification for spalling severity segmentation. Identifying masonry joint locations is a necessary first step of this method, as it enables analysis to be conducted on each block independently and separates the blocks from the mortar areas. Masonry joint segmentation also enables individual block labelling and documentation, which provides additional localization information to assessing engineers and maintenance teams, while helping to build a model of the structure that can be used for numerical analysis (Loverdos and Sarhosis, 2023). While multiple studies have analysed automated masonry joint segmentation methods, these have largely involved photographic data (Ibrahim et al., 2020; Kajatin and Nalpantidis, 2021). While these are shown to be broadly effective, they do not make full use of the 3D nature of point cloud data. Recent advances in deep learning methods have enabled semantic segmentation to be conducted directly on 3D point clouds. An issue with many of these methods is that they require a substantial amount of data labelling to achieve sufficient performance. This is a manual task that would erode savings in the overall condition assessment labour time. Due to these issues, in addition to the difficulties in generalizing a model to the large variety of masonry types, this paper analyses the situation where a model is trained on a short section of tunnel, before application to automate analysis of the remaining area of the same tunnel. With a focus on performance with limited training data, this paper assesses the feasibility of using supervised machine learning for masonry joint semantic segmentation from lidar data. Different methods are analysed when only trained on a 9.94m section of tunnel and applied to segment joints within the rest of the tunnel. This paper compares 2D and 3D deep learning methods with feature-based machine learning methods.