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Structural Engineering and Mechanics Volume 91, Number 2, July25 2024 , pages 163-175 DOI: https://doi.org/10.12989/sem.2024.91.2.163 |
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Transfer learning for crack detection in concrete structures: Evaluation of four models |
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Ali Bagheri, Mohammadreza Mosalmanyazdi and Hasanali Mosalmanyazdi
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| Abstract | ||
| The objective of this research is to improve public safety in civil engineering by recognizing fractures in concrete structures quickly and correctly. The study offers a new crack detection method based on advanced image processing and machine learning techniques, specifically transfer learning with convolutional neural networks (CNNs). Four pre-trained models (VGG16, AlexNet, ResNet18, and DenseNet161) were fine-tuned to detect fractures in concrete surfaces. These models constantly produced accuracy rates greater than 80%, showing their ability to automate fracture identification and potentially reduce structural failure costs. Furthermore, the study expands its scope beyond crack detection to identify concrete health, using a dataset with a wide range of surface defects and anomalies including cracks. Notably, using VGG16, which was chosen as the most effective network architecture from the first phase, the study achieves excellent accuracy in classifying concrete health, demonstrating the model's satisfactorily performance even in more complex scenarios. | ||
| Key Words | ||
| concrete structures; convolutional neural networks; crack detection; structural health monitoring; transfer learning | ||
| Address | ||
| Ali Bagheri, Mohammadreza Mosalmanyazdi and Hasanali Mosalmanyazdi: Department of Civil Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran | ||