Journal
AUTOMATION IN CONSTRUCTION
Volume 105, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.autcon.2019.04.019
Keywords
Concrete defect classification; Automated bridge inspection; Defect detection; Crack detection; Spalling detection; Scaling detection; Efflorescence detection; Rust staining detection; Exposed reinforcement detection
Funding
- EPSRC
- Trimble Inc.
- Cambridge Trimble Fund
- Infravation SeeBridge project [31109806.0007]
- ERA-NET Plus Infravation
- European Commission
- Ministerie van Infrastructuur en Milieu
- Rijkswaterstaat
- Bundesministerium fr Verkehr
- Bauund Stadtentwickltmg
- 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.
- Federal Highway Administration USDOT
- Engineering and Physical Sciences Research Council [1481532] Funding Source: researchfish
Ask authors/readers for more resources
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.
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