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Proceedings 2018, 2(8), 418; https://doi.org/10.3390/ICEM18-05281

Development of Delamination Detection System for Concrete Decks by Using Convolutional Neural Network

1
Faculty of Engineering, The University of Tokyo, Tokyo 153-8505, Japan
2
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
3
Nippon Engineering Consultants CO., LTD., Tokyo 170-0003, Japan
4
National Institute of Informatics, Tokyo 101-8430, Japan
5
Nagaoka National College of Technology, Niigata 940-8532, Japan
6
Barcelona School of Informatics, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Presented at the 18th International Conference on Experimental Mechanics, Brussels, Belgium, 1–5 July 2018. Published: 28 May
*
Author to whom correspondence should be addressed.
Published: 28 May 2018
PDF [773 KB, uploaded 19 June 2018]

Abstract

Bridges in Japan, especially those managed by municipalities, deteriorate over time. Due to lack of civil engineers in municipalities, appropriate and automated assistance for degradation judgement is thought to be important for the concerned authorities. Automated judgement systems for some types of damage (e.g., cracks) started to be developed by geometrical approaches. Yet, there is no comprehensive method to detect more complicated types of damage, such as delamination, for regular inspection. This research aims to develop a delamination-detection system which identifies the location of the damage. Images with delaminated parts were provided by Niigata Prefecture (in Japan), and annotation of the location of delamination and/or rebar exposure was conducted. Fully Convolutional Network (FCN), one of the deep learning networks for pixel-to-pixel segmentation, was used to detect the areas of the delamination and rebar exposure. The result of the training aided by FCN showed a good agreement with the result with the naked eye. The soundness, judged based on the FCN result according to the inspection code of Niigata Prefecture, was close to the soundness judgement at the site. These outcomes support the reliability of the system to detect delamination and rebar exposure in manual inspection. This technology is expected to be used in bridges’ inspection at municipalities, which have a lack of inspection engineers.
Keywords: damage detection; delamination; regular inspection; machine learning; fully convolutional network damage detection; delamination; regular inspection; machine learning; fully convolutional network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kashiwa, T.; Nagai, K.; Tatsuta, H.; Prendinger, H.; Ibayashi, K.; Guillamón, J.J.R. Development of Delamination Detection System for Concrete Decks by Using Convolutional Neural Network. Proceedings 2018, 2, 418.

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