Machine learning application for concrete surface defects automatic damage classification
Defects damage in concrete structures are an important measure of structural integrity and serviceability. In the context of investigating the condition of concrete surface that has defects, a visual inspection is usually performed. However, this method is subjective, tedious, time-consuming, and co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/25120/ http://journalarticle.ukm.my/25120/1/03.pdf |
| _version_ | 1848816274931449856 |
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| author | Syahrul Fithry Senin, Khairullah Yusuf, Amer Yusuf, Rohamezan Rohim, |
| author_facet | Syahrul Fithry Senin, Khairullah Yusuf, Amer Yusuf, Rohamezan Rohim, |
| author_sort | Syahrul Fithry Senin, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Defects damage in concrete structures are an important measure of structural integrity and serviceability. In the context of investigating the condition of concrete surface that has defects, a visual inspection is usually performed. However, this method is subjective, tedious, time-consuming, and complicated, requiring access to many components of a large project design. Therefore, a Machine Learning classifier for concrete surface defect classification using the Discriminant Analysis Classifier was introduced to more accurately extract the types of concrete surface defects information from the digital images. The aim of this research is to increase the efficiency of concrete surface defect analysis in terms of quality, time and cost. 200 images were collected, with 50 images for each concrete defect (crack, corrosion, spalling, and no defect) serving as control data. The Gray Level Co-Occurrence Matrix (GLCM) is used to create an image processing and feature extraction algorithm. This model is trained using 80% of the image data and tested using another 20% of the image data. Thus, the model achieved 95% accuracy on the training data and 70% on the test data when using Quadratic Discriminant Analysis. These findings is very important to help engineers or construction inspectors in inspection activities. |
| first_indexed | 2025-11-15T01:03:17Z |
| format | Article |
| id | oai:generic.eprints.org:25120 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:03:17Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:251202025-05-26T06:40:54Z http://journalarticle.ukm.my/25120/ Machine learning application for concrete surface defects automatic damage classification Syahrul Fithry Senin, Khairullah Yusuf, Amer Yusuf, Rohamezan Rohim, Defects damage in concrete structures are an important measure of structural integrity and serviceability. In the context of investigating the condition of concrete surface that has defects, a visual inspection is usually performed. However, this method is subjective, tedious, time-consuming, and complicated, requiring access to many components of a large project design. Therefore, a Machine Learning classifier for concrete surface defect classification using the Discriminant Analysis Classifier was introduced to more accurately extract the types of concrete surface defects information from the digital images. The aim of this research is to increase the efficiency of concrete surface defect analysis in terms of quality, time and cost. 200 images were collected, with 50 images for each concrete defect (crack, corrosion, spalling, and no defect) serving as control data. The Gray Level Co-Occurrence Matrix (GLCM) is used to create an image processing and feature extraction algorithm. This model is trained using 80% of the image data and tested using another 20% of the image data. Thus, the model achieved 95% accuracy on the training data and 70% on the test data when using Quadratic Discriminant Analysis. These findings is very important to help engineers or construction inspectors in inspection activities. Penerbit Universiti Kebangsaan Malaysia 2024 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/25120/1/03.pdf Syahrul Fithry Senin, and Khairullah Yusuf, and Amer Yusuf, and Rohamezan Rohim, (2024) Machine learning application for concrete surface defects automatic damage classification. Jurnal Kejuruteraan, 36 (1). pp. 21-27. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3601-2024 |
| spellingShingle | Syahrul Fithry Senin, Khairullah Yusuf, Amer Yusuf, Rohamezan Rohim, Machine learning application for concrete surface defects automatic damage classification |
| title | Machine learning application for concrete surface defects automatic damage classification |
| title_full | Machine learning application for concrete surface defects automatic damage classification |
| title_fullStr | Machine learning application for concrete surface defects automatic damage classification |
| title_full_unstemmed | Machine learning application for concrete surface defects automatic damage classification |
| title_short | Machine learning application for concrete surface defects automatic damage classification |
| title_sort | machine learning application for concrete surface defects automatic damage classification |
| url | http://journalarticle.ukm.my/25120/ http://journalarticle.ukm.my/25120/ http://journalarticle.ukm.my/25120/1/03.pdf |