loading page

Deep Learning Models for Analysis of Non-Destructive Evaluation Data of Reinforced Concrete Bridge Decks: A Survey
  • Dayakar Naik Lavadiya,
  • Sattar Dorafshan
Dayakar Naik Lavadiya
University of North Dakota

Corresponding Author:[email protected]

Author Profile
Sattar Dorafshan
University of North Dakota
Author Profile


Application of deep learning (DL) for automatic condition assessment of bridge infrastructure has been on the rise in the last few years. From the published literature, it is evident that lot of research efforts has been put in identifying the surface defects such as cracks, potholes, spalling etc. using deep learning. However, a concrete bridge deck health is jeopardized by the presence of subsurface defects substantially, however, the task of defect detection using deep learning has not received the proper attention. The goal of this survey paper is to provide a critical review of existing technical knowledge for DL application on NDE data for bridge deck evaluation. The authors reviewed prominent NDE techniques for subsurface defect detection of bridge decks and explored the various DL models proposed to identify these defects. First a brief overview of the working principle of NDE techniques and DL architectures is provided, and then the information about proposed DL models and their efficacy is highlighted. Based on the existing knowledge gaps, various challenges and future prospects associated with application of DL in bridge subsurface inspection are discussed.
01 Aug 2022Submitted to Engineering Reports
03 Aug 2022Submission Checks Completed
03 Aug 2022Assigned to Editor
03 Aug 2022Reviewer(s) Assigned
26 Oct 20221st Revision Received
27 Oct 2022Submission Checks Completed
27 Oct 2022Assigned to Editor
27 Oct 2022Review(s) Completed, Editorial Evaluation Pending
31 Oct 2022Reviewer(s) Assigned
28 Nov 2022Editorial Decision: Accept