TensorFlow-Native Implementation for Crack Detection in Concrete Structures
This paper presents a TensorFlow-native implementation for automated crack detection in concrete structures, addressing the critical need for efficient and objective infrastructure monitoring. Leveraging a Convolutional Neural Network architecture with 24.8 million parameters, the model was trained...
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| Format: | Article |
| Language: | English |
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Mesopotamian Journal of Civil Engineering
2026
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| Online Access: | http://hdl.handle.net/20.500.12493/3053 |
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| author | Ayebare, Memory Chavula, Petros Mugisha, Simon Byamukama, Willbroad |
| author_facet | Ayebare, Memory Chavula, Petros Mugisha, Simon Byamukama, Willbroad |
| author_sort | Ayebare, Memory |
| collection | KAB-DR |
| description | This paper presents a TensorFlow-native implementation for automated crack detection in concrete structures, addressing the critical need for efficient and objective infrastructure monitoring. Leveraging a Convolutional Neural Network architecture with 24.8 million parameters, the model was trained on a large-scale dataset of 40,000 images, each with a 227x227 RGB resolution. The methodology, incorporating specific framework optimizations and a rigorous training configuration, achieved a remarkable overall classification accuracy of 99.375% on the validation dataset. The model demonstrated balanced performance with precision values of 0.993 and 0.994, recall values of 0.994 and 0.993, and F1-scores of 0.994 and 0.994 for both "No Crack" and "Crack" classes. This high accuracy, coupled with balanced metrics, underscores the model's effectiveness and reliability for practical applications. The proposed solution significantly enhances real-time structural health monitoring systems, mitigating the limitations of traditional manual inspections and facilitating proactive maintenance strategies for concrete infrastructure |
| format | Article |
| id | oai:idr.kab.ac.ug:20.500.12493-3053 |
| institution | KAB-DR |
| language | English |
| publishDate | 2026 |
| publisher | Mesopotamian Journal of Civil Engineering |
| record_format | dspace |
| spelling | oai:idr.kab.ac.ug:20.500.12493-30532026-01-04T00:00:34Z TensorFlow-Native Implementation for Crack Detection in Concrete Structures Ayebare, Memory Chavula, Petros Mugisha, Simon Byamukama, Willbroad Concrete crack detection Convolutional Neural Network TensorFlow Structural health monitoring Deep learning. This paper presents a TensorFlow-native implementation for automated crack detection in concrete structures, addressing the critical need for efficient and objective infrastructure monitoring. Leveraging a Convolutional Neural Network architecture with 24.8 million parameters, the model was trained on a large-scale dataset of 40,000 images, each with a 227x227 RGB resolution. The methodology, incorporating specific framework optimizations and a rigorous training configuration, achieved a remarkable overall classification accuracy of 99.375% on the validation dataset. The model demonstrated balanced performance with precision values of 0.993 and 0.994, recall values of 0.994 and 0.993, and F1-scores of 0.994 and 0.994 for both "No Crack" and "Crack" classes. This high accuracy, coupled with balanced metrics, underscores the model's effectiveness and reliability for practical applications. The proposed solution significantly enhances real-time structural health monitoring systems, mitigating the limitations of traditional manual inspections and facilitating proactive maintenance strategies for concrete infrastructure 2026-01-03T14:58:24Z 2026-01-03T14:58:24Z 2025 Article Ayebare, M., Chavula, P., Mugisha, S., & Willbroad, B. (2025). TensorFlow-Native Implementation for Crack Detection in Concrete Structures. Mesopotamian Journal of Civil Engineering, 2025, 97-108. 3006-1148 http://hdl.handle.net/20.500.12493/3053 en Attribution-NonCommercial-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ application/pdf Mesopotamian Journal of Civil Engineering |
| spellingShingle | Concrete crack detection Convolutional Neural Network TensorFlow Structural health monitoring Deep learning. Ayebare, Memory Chavula, Petros Mugisha, Simon Byamukama, Willbroad TensorFlow-Native Implementation for Crack Detection in Concrete Structures |
| title | TensorFlow-Native Implementation for Crack Detection in Concrete Structures |
| title_full | TensorFlow-Native Implementation for Crack Detection in Concrete Structures |
| title_fullStr | TensorFlow-Native Implementation for Crack Detection in Concrete Structures |
| title_full_unstemmed | TensorFlow-Native Implementation for Crack Detection in Concrete Structures |
| title_short | TensorFlow-Native Implementation for Crack Detection in Concrete Structures |
| title_sort | tensorflow native implementation for crack detection in concrete structures |
| topic | Concrete crack detection Convolutional Neural Network TensorFlow Structural health monitoring Deep learning. |
| url | http://hdl.handle.net/20.500.12493/3053 |
| work_keys_str_mv | AT ayebarememory tensorflownativeimplementationforcrackdetectioninconcretestructures AT chavulapetros tensorflownativeimplementationforcrackdetectioninconcretestructures AT mugishasimon tensorflownativeimplementationforcrackdetectioninconcretestructures AT byamukamawillbroad tensorflownativeimplementationforcrackdetectioninconcretestructures |