Automated Detection and Quantification of Cracks and Spalls in Concrete Bridge Decks Using Deep Learning
This paper presents a deep learning approach for automated detection and quantification of cracks and spalls in concrete bridge decks. The proposed concrete defect detection approach is based on the integration of convolutional neural network with a long short-term memory architecture. Thousands of manually labeled images collected from the concrete structures in Pittsburgh are used to calibrate the deep learning algorithm. The results indicate that the developed deep learning algorithm is capable of identifying the cracks and spalls on the concrete surface with acceptable accuracy. A calculation procedure is developed to quantify the density of the cracks and spalled areas that is required in the current Pennsylvania Department of Transportation (PennDOT) condition rating system for concrete bridge decks. Furthermore, a software program is designed to facilitate the implementation of the proposed method. The fast implementation of the developed deep learning framework makes it a promising tool for automated and real-time bridge and pavement inspections.
- Record URL:
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Supplemental Notes:
- This paper was sponsored by TRB committee AED50 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
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Corporate Authors:
Transportation Research Board
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Authors:
- Zhang, Qianyun
- Babanajad, Saeed
- Moon, Franklin
- 0000-0003-3180-9120
- Alavi, Amir H
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Conference:
- Transportation Research Board 100th Annual Meeting
- Location: Washington DC, United States
- Date: 2021-1-5 to 2021-1-29
- Date: 2021
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Photos; References;
- Pagination: 18p
Subject/Index Terms
- TRT Terms: Algorithms; Automation; Bridge decks; Concrete pavements; Cracking; Flaw detection; Machine learning; Spalling
- Geographic Terms: Pittsburgh (Pennsylvania)
- Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation; Pavements;
Filing Info
- Accession Number: 01764116
- Record Type: Publication
- Report/Paper Numbers: TRBAM-21-00490
- Files: TRIS, TRB, ATRI
- Created Date: Feb 4 2021 11:00AM