2D Digital Image Correlation and Region-Based Convolutional Neural Network in Monitoring and Evaluation of Surface Cracks in Concrete Structural Elements
This paper shows how 2D digital image correlation (2D DIC) and region-based convolutional neural network (R-CNN) can be combined for image-based automated monitoring and assessment of surface crack development of concrete structural elements during laboratory quasi-static tests. In the presented app...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/13/16/3527 |
| _version_ | 1797559180106661888 |
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| author | Marek Słoński Marcin Tekieli |
| author_facet | Marek Słoński Marcin Tekieli |
| author_sort | Marek Słoński |
| collection | DOAJ |
| description | This paper shows how 2D digital image correlation (2D DIC) and region-based convolutional neural network (R-CNN) can be combined for image-based automated monitoring and assessment of surface crack development of concrete structural elements during laboratory quasi-static tests. In the presented approach, the 2D DIC-based monitoring enables estimation of deformation fields on the surface of the concrete element and measurements of crack width. Moreover, the R-CNN model provides unmanned simultaneous detection and localization of multiple cracks in the images. The results show that the automatic monitoring and evaluation of crack development in concrete structural elements is possible with high accuracy and reliability. |
| first_indexed | 2024-03-10T17:41:45Z |
| format | Article |
| id | doaj.art-6dac817154294e9cbb5575f8396cb492 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T17:41:45Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-6dac817154294e9cbb5575f8396cb4922023-11-20T09:40:43ZengMDPI AGMaterials1996-19442020-08-011316352710.3390/ma131635272D Digital Image Correlation and Region-Based Convolutional Neural Network in Monitoring and Evaluation of Surface Cracks in Concrete Structural ElementsMarek Słoński0Marcin Tekieli1Faculty of Civil Engineering, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, PolandFaculty of Civil Engineering, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, PolandThis paper shows how 2D digital image correlation (2D DIC) and region-based convolutional neural network (R-CNN) can be combined for image-based automated monitoring and assessment of surface crack development of concrete structural elements during laboratory quasi-static tests. In the presented approach, the 2D DIC-based monitoring enables estimation of deformation fields on the surface of the concrete element and measurements of crack width. Moreover, the R-CNN model provides unmanned simultaneous detection and localization of multiple cracks in the images. The results show that the automatic monitoring and evaluation of crack development in concrete structural elements is possible with high accuracy and reliability.https://www.mdpi.com/1996-1944/13/16/3527digital image correlationregion-based convolutional neural networkmachine learningcrack monitoringcrack detection and localization |
| spellingShingle | Marek Słoński Marcin Tekieli 2D Digital Image Correlation and Region-Based Convolutional Neural Network in Monitoring and Evaluation of Surface Cracks in Concrete Structural Elements Materials digital image correlation region-based convolutional neural network machine learning crack monitoring crack detection and localization |
| title | 2D Digital Image Correlation and Region-Based Convolutional Neural Network in Monitoring and Evaluation of Surface Cracks in Concrete Structural Elements |
| title_full | 2D Digital Image Correlation and Region-Based Convolutional Neural Network in Monitoring and Evaluation of Surface Cracks in Concrete Structural Elements |
| title_fullStr | 2D Digital Image Correlation and Region-Based Convolutional Neural Network in Monitoring and Evaluation of Surface Cracks in Concrete Structural Elements |
| title_full_unstemmed | 2D Digital Image Correlation and Region-Based Convolutional Neural Network in Monitoring and Evaluation of Surface Cracks in Concrete Structural Elements |
| title_short | 2D Digital Image Correlation and Region-Based Convolutional Neural Network in Monitoring and Evaluation of Surface Cracks in Concrete Structural Elements |
| title_sort | 2d digital image correlation and region based convolutional neural network in monitoring and evaluation of surface cracks in concrete structural elements |
| topic | digital image correlation region-based convolutional neural network machine learning crack monitoring crack detection and localization |
| url | https://www.mdpi.com/1996-1944/13/16/3527 |
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