Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey
Abstract Application of deep learning (DL) for automatic condition assessment of bridge decks has been on the raise in the last few years. From the published literature, it is evident that lot of research efforts has been done in identifying the surface defects such as cracks, potholes, spalling and...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Engineering Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/eng2.12608 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832576632799363072 |
|---|---|
| author | Dayakar Naik Lavadiya Sattar Dorafshan |
| author_facet | Dayakar Naik Lavadiya Sattar Dorafshan |
| author_sort | Dayakar Naik Lavadiya |
| collection | DOAJ |
| description | Abstract Application of deep learning (DL) for automatic condition assessment of bridge decks has been on the raise in the last few years. From the published literature, it is evident that lot of research efforts has been done in identifying the surface defects such as cracks, potholes, spalling and so forth using supervised learning methods such as deep learning. However, the health of a reinforced concrete bridge deck is jeopardized substantially due to presence of subsurface defects. Subsurface defects in bridge decks are genearlly detected using non‐destructive evaluation (NDE) methods. Interpertation of NDE data for autonomous deck evaluation requires development of DL models; however, The task of defect detection DL has not received the proper attention for subsurface defect detection in the past. The goal of this paper is to provide a review of existing DL models for analysis of NDE data of bridge decks. 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. |
| format | Article |
| id | doaj-art-8d1e8c931fec496185d92b4c2fe1b835 |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-8d1e8c931fec496185d92b4c2fe1b8352025-01-31T00:22:48ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.12608Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A surveyDayakar Naik Lavadiya0Sattar Dorafshan1Department of Civil & Mechanical Engineering University of Mary Bismarck North Dakota USADepartment of Civil Engineering University of North Dakota Grand Forks North Dakota USAAbstract Application of deep learning (DL) for automatic condition assessment of bridge decks has been on the raise in the last few years. From the published literature, it is evident that lot of research efforts has been done in identifying the surface defects such as cracks, potholes, spalling and so forth using supervised learning methods such as deep learning. However, the health of a reinforced concrete bridge deck is jeopardized substantially due to presence of subsurface defects. Subsurface defects in bridge decks are genearlly detected using non‐destructive evaluation (NDE) methods. Interpertation of NDE data for autonomous deck evaluation requires development of DL models; however, The task of defect detection DL has not received the proper attention for subsurface defect detection in the past. The goal of this paper is to provide a review of existing DL models for analysis of NDE data of bridge decks. 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.https://doi.org/10.1002/eng2.12608bridge deckdelaminationimpact echo and ground penetrating radarinfrared thermographyrebar |
| spellingShingle | Dayakar Naik Lavadiya Sattar Dorafshan Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey Engineering Reports bridge deck delamination impact echo and ground penetrating radar infrared thermography rebar |
| title | Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey |
| title_full | Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey |
| title_fullStr | Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey |
| title_full_unstemmed | Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey |
| title_short | Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey |
| title_sort | deep learning models for analysis of non destructive evaluation data to evaluate reinforced concrete bridge decks a survey |
| topic | bridge deck delamination impact echo and ground penetrating radar infrared thermography rebar |
| url | https://doi.org/10.1002/eng2.12608 |
| work_keys_str_mv | AT dayakarnaiklavadiya deeplearningmodelsforanalysisofnondestructiveevaluationdatatoevaluatereinforcedconcretebridgedecksasurvey AT sattardorafshan deeplearningmodelsforanalysisofnondestructiveevaluationdatatoevaluatereinforcedconcretebridgedecksasurvey |