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Multi-classifier for reinforced concrete bridge defects

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journal contribution
posted on 09.05.2019, 11:11 by Philipp Huthwohl, Ruodan Lu, Ioannis Brilakis
Classifying concrete defects during a bridge inspection remains a subjective and laborious task. The risk of getting a false result is approximately 50% if different inspectors assess the same concrete defect. This is significant in the light of an over-aging bridge stock, decreasing infrastructure maintenance budgets and catastrophic bridge collapses as happened in 2018 in Genoa, Italy. To support an automated inspection and an objective bridge defect classification, we propose a three-staged concrete defect classifier that can multi classify potentially unhealthy bridge areas into their specific defect type in conformity with existing bridge inspection guidelines. Three separate deep neural pre-trained networks are fine-tuned based on a multi source dataset consisting of self-collected image samples plus several Departments of Transportation inspection databases. We show that this approach can reliably classify multiple defect types with an average mean score of 85%. Our presented multi-classifier is a contribution towards developing a mostly or fully inspection schema for a more cost-effective and more objective bridge inspection.

Funding

This research work is supported by EPSRC, Trimble Inc., Cambridge Trimble Fund, and Infravation SeeBridge project under Grant Number No. 31109806.0007. SeeBridge is co-funded by Funding Partners of the ERA-NET Plus Infravation and the European Commission. The Funding Partners of the Infravation 2014 Call are: Ministerie van Infrastructuur en Milieu, Rijkswaterstaat, Bundesministerium fr Verkehr, Bauund Stadtentwicklung, Danish Road Directorate, Statens Vegvesen Vegdirektoratet, Trafikverket Trv, Vegagerin, Ministere de Lecologie, du Developpement Durable et de Lenergie, Centropara el Desarrollo Tecnologico Industrial, Anas S.P.A., Netivei Israel National Transport Infrastructure Company Ltd. and Federal Highway Administration USDOT.

History

School

  • Architecture, Building and Civil Engineering

Published in

Automation in Construction

Volume

105

Citation

HUTHWOHL, P., LU, R. and BRILAKIS, I., 2019. Multi-classifier for reinforced concrete bridge defects. Automation in Construction, 105, 102824.

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This paper was accepted for publication in the journal Automation in Construction and the definitive published version is available at https://doi.org/10.1016/j.autcon.2019.04.019.

Acceptance date

21/04/2019

Publication date

2019-05-09

ISSN

0926-5805

Language

en

Article number

102824