Technology of crack detection in reinforced concrete structures
Some crucial signs of structural failure that are critical for repair would be cracks on the structures as well as constant exposure that can result in severe environmental damage. Being able to detect cracks on structures is becoming an essential aspect of the technology of the construction industr...
| Main Authors: | , , , , , |
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| Format: | Book Section |
| Language: | English |
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Penerbit UTHM
2020
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| Online Access: | http://eprints.uthm.edu.my/2065/ http://eprints.uthm.edu.my/2065/1/Ch11%20Technology%20of%20crack%20detection.pdf |
| _version_ | 1848887633814487040 |
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| author | Wan Jusoh, Wan Arnizah Hanipah, Mohd Hafizal Zakaria, Mohd Azuan Pakir, Faizal Adnan, Suraya Hani Osman, Mohamad Hairi |
| author2 | Tuan Ismail, Tuan Noor Hasanah |
| author_facet | Tuan Ismail, Tuan Noor Hasanah Wan Jusoh, Wan Arnizah Hanipah, Mohd Hafizal Zakaria, Mohd Azuan Pakir, Faizal Adnan, Suraya Hani Osman, Mohamad Hairi |
| author_sort | Wan Jusoh, Wan Arnizah |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Some crucial signs of structural failure that are critical for repair would be cracks on the structures as well as constant exposure that can result in severe environmental damage. Being able to detect cracks on structures is becoming an essential aspect of the technology of the construction industry. Destructive Testing and Non-Destructive Testing are the two methods used for structural crack detection. This study focused on the techniques used to detect cracks. Several effective methods to detect cracks were carried out and compared to identify the most suitable method in detecting cracks on structures within the demographics of Malaysia. Image processing techniques (IPTs) through the photogrammetry method, surface crack analysis program and Convolution Neural Network (CNN) were carried out to examine crack detection through measurement and monitoring from images. The distance was determined in this study for the physical properties, using both conductibility and accuracy. The photogrammetry method was able to conduct distance at 0.1 - 40 m, with an accuracy of up to 0.005 mm. Therefore, the surface cracks analysis provided 0.10 mm accuracy, while results on CNN had an accuracy of 0.95 mm (98.22 % and 97.95 % in training and validation). Results from physical properties showed that photogrammetry had the highest accuracy, while CNN has the least accuracy. Hence, this study concluded that Photogrammetry method and Convolution Neural Network (CNN) were both the most effective methods to be used in providing clear information and effective ways to detect crack on structures. |
| first_indexed | 2025-11-15T19:57:30Z |
| format | Book Section |
| id | uthm-2065 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:57:30Z |
| publishDate | 2020 |
| publisher | Penerbit UTHM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-20652021-11-01T01:37:38Z http://eprints.uthm.edu.my/2065/ Technology of crack detection in reinforced concrete structures Wan Jusoh, Wan Arnizah Hanipah, Mohd Hafizal Zakaria, Mohd Azuan Pakir, Faizal Adnan, Suraya Hani Osman, Mohamad Hairi TH1000-1725 Systems of building construction. Including fireproof construction, concrete construction Some crucial signs of structural failure that are critical for repair would be cracks on the structures as well as constant exposure that can result in severe environmental damage. Being able to detect cracks on structures is becoming an essential aspect of the technology of the construction industry. Destructive Testing and Non-Destructive Testing are the two methods used for structural crack detection. This study focused on the techniques used to detect cracks. Several effective methods to detect cracks were carried out and compared to identify the most suitable method in detecting cracks on structures within the demographics of Malaysia. Image processing techniques (IPTs) through the photogrammetry method, surface crack analysis program and Convolution Neural Network (CNN) were carried out to examine crack detection through measurement and monitoring from images. The distance was determined in this study for the physical properties, using both conductibility and accuracy. The photogrammetry method was able to conduct distance at 0.1 - 40 m, with an accuracy of up to 0.005 mm. Therefore, the surface cracks analysis provided 0.10 mm accuracy, while results on CNN had an accuracy of 0.95 mm (98.22 % and 97.95 % in training and validation). Results from physical properties showed that photogrammetry had the highest accuracy, while CNN has the least accuracy. Hence, this study concluded that Photogrammetry method and Convolution Neural Network (CNN) were both the most effective methods to be used in providing clear information and effective ways to detect crack on structures. Penerbit UTHM Tuan Ismail, Tuan Noor Hasanah Osman, Mohamad Hairi Yuriz, Yasmin Md Amin, Harina Adnan, Suraya Hani 2020 Book Section PeerReviewed text en http://eprints.uthm.edu.my/2065/1/Ch11%20Technology%20of%20crack%20detection.pdf Wan Jusoh, Wan Arnizah and Hanipah, Mohd Hafizal and Zakaria, Mohd Azuan and Pakir, Faizal and Adnan, Suraya Hani and Osman, Mohamad Hairi (2020) Technology of crack detection in reinforced concrete structures. In: Construction Materials and Technology. Penerbit UTHM, pp. 117-134. ISBN 978-967-2389-63-7 |
| spellingShingle | TH1000-1725 Systems of building construction. Including fireproof construction, concrete construction Wan Jusoh, Wan Arnizah Hanipah, Mohd Hafizal Zakaria, Mohd Azuan Pakir, Faizal Adnan, Suraya Hani Osman, Mohamad Hairi Technology of crack detection in reinforced concrete structures |
| title | Technology of crack detection in reinforced concrete structures |
| title_full | Technology of crack detection in reinforced concrete structures |
| title_fullStr | Technology of crack detection in reinforced concrete structures |
| title_full_unstemmed | Technology of crack detection in reinforced concrete structures |
| title_short | Technology of crack detection in reinforced concrete structures |
| title_sort | technology of crack detection in reinforced concrete structures |
| topic | TH1000-1725 Systems of building construction. Including fireproof construction, concrete construction |
| url | http://eprints.uthm.edu.my/2065/ http://eprints.uthm.edu.my/2065/1/Ch11%20Technology%20of%20crack%20detection.pdf |