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.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Photos; References;
  • Pagination: 18p

Subject/Index Terms

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