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Lookup NU author(s): Thomas McDonaldORCiD, Professor Mark RobinsonORCiD, Professor Gui Yun TianORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2022 by the authors. Featured Application: The review presented in this work has practical application to the conception, development and refinement of new technologies and visualisation frameworks pertaining to railway tunnel subsurface inspection. Subsequent application to the development of prototype self-sustaining digital twin tunnels also presents opportunity. In both cases, practical end user benefit would be improvement to the clarity and comprehensiveness of subsurface inspection datasets, better informing targeted maintenance strategy planning. Railway Tunnel SubSurface Inspection (RTSSI) is essential for targeted structural maintenance. ‘Effective’ detection, localisation and characterisation of fully concealed features (i.e., assets, defects) is the primary challenge faced by RTSSI engineers, particularly in historic masonry tunnels. Clear conveyance and communication of gathered information to end-users poses the less frequently considered secondary challenge. The purpose of this review is to establish the current state of the art in RTSSI data acquisition and information conveyance schemes, in turn formalising exactly what constitutes an ‘effective’ RTSSI visualisation framework. From this knowledge gaps, trends in leading RTSSI research and opportunities for future development are explored. Literary analysis of over 300 resources (identified using the 360-degree search method) informs data acquisition system operation principles, common strengths and limitations, alongside leading studies and commercial tools. Similar rigor is adopted to appraise leading information conveyance schemes. This provides a comprehensive whilst critical review of present research and future development opportunities within the field. This review highlights common shortcomings shared by multiple methods for RTSSI, which are used to formulate robust criteria for a contextually ‘effective’ visualisation framework. Although no current process is deemed fully effective; a feasible hybridised framework capable of meeting all stipulated criteria is proposed based on identified future research avenues. Scope for novel analysis of helical point cloud subsurface datasets obtained by a new rotating ground penetrating radar antenna is of notable interest.
Author(s): McDonald T, Robinson M, Tian GY
Publication type: Review
Publication status: Published
Journal: Applied Sciences
Year: 2022
Volume: 12
Issue: 22
Online publication date: 08/11/2022
Acceptance date: 01/11/2022
ISSN (electronic): 2076-3417
Publisher: MDPI
URL: https://doi.org/10.3390/app122211310
DOI: 10.3390/app122211310
