Panella, F;
Loo, Y;
Devriendt, M;
Gonzalez, D;
Kaushik, A;
Ollerhead, R;
Boehm, J;
(2018)
Smart Image Based Technology and Deep Learning for Tunnel Inspection and Asset Management.
In:
Proceedings of the 16th World Conference Of the Associated Research Centers for the Urban Underground Space.
(pp. pp. 894-904).
ACCUS
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Abstract
Tunnel inspection and asset management is typically a labour-intensive process where engineering judgement and experience is heavily relied upon to identify and assess tunnel condition over kilometres of homogeneous structures. Novel work flows and digital applications have been developed by the authors to create various smart image-based inspection and analysis tools that reduce the potential subjectivity and inconsistency of these inspections. This has resulted in significant improvements to existing tunnel inspection practices and structural health assessment. Current advances in image capture technology and computational processing power has enabled high integrity data to be easily captured, visualised and analysed. The work flows and tools developed take advantage of existing low-cost image capture hardware, open-source processing software and couples this with the creation of unique machine learning algorithms and analytics. Core innovations include: (i) use of low-cost photographic equipment for high quality imagery capture (ii) use of automated inspection vehicles for data capture (iii) Deep learning for automatic defect object recognition and defect classification (iv) Creation of immersive dashboards and 3D visualisations. This results in a suite of image based service offerings and deliverables, relevant to specific tunnel engineering issues and asset management aims. Thanks to deep learning, defect detection and asset condition metrics are automatically created, enabling: (i) the tunnel owner to gain greater insights into their asset resilience and operations, (ii) the tunnel engineer to focus on key issues aided by machine learning.
| Type: | Proceedings paper |
|---|---|
| Title: | Smart Image Based Technology and Deep Learning for Tunnel Inspection and Asset Management |
| Event: | 16th World Conference Of the Associated Research Centers for the Urban Underground Space. Integrated Underground Solutions for Compact Metropolitan Cities |
| Location: | Hong Kong, China |
| Dates: | 5th-7th November 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://acuus2018.hk/ACUUS2018-proceeding_final.pdf |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Deep Learning, Automated Crack Detection, Photogrammetric Tunnelling Surveys, Underground Infrastructure, Safety and Human Factors, Technical Approaches and Innovations |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10069513 |
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