Abstract
The reliable and timely detection of cracks in concrete structures is essential for maintaining the safety, functionality, and longevity of civil infrastructure, including buildings, bridges, highways, and dams. Structural cracks can emerge due to multiple factors such as material fatigue, environmental stressors, seismic activity, and thermal expansion, necessitating accurate and efficient monitoring systems. Traditional inspection techniques, including manual visual inspection and non-destructive testing, are labour-intensive, prone to subjectivity, and often lack scalability. To address these limitations, the research presents CrackNet-VGG, a deep learning-based framework that leverages the VGG16 convolutional neural network architecture for automatic binary classification of surface cracks in concrete images. The proposed model leverages transfer learning by fine-tuning the VGG16 architecture on concrete surface datasets, utili sing its convolutional layers for robust feature extraction and its fully connected layers for final binary classification. The model is trained and evaluated on publicly available benchmark datasets, categorised into two classes: cracked and non-cracked surfaces. Experimental results demonstrate that CrackNet-VGG achieves a high classification accuracy of 96.07 %, with 95.57 % precision and 95.31 % recall, surpassing several baseline deep learning models in terms of accuracy. These results validate the applicability of CrackNet-VGG as an effective solution for automated concrete crack detection in real-world scenarios.