Image classification for sub-surface crack identification in concrete dam based on borehole CCTV images using deep dense hybrid model

Abstract

The research investigates the significance of identifying structure discontinuities, such as cracks, in concrete dams to ensure dam safety and stability. A novel automatic image classification method is developed, employing Deep Dense Transfer Learning (DDTL) with pre-trained models, including EfficientNetB1, ResNet50, and a hybrid model to identify the detection of cracks in sub-surfaces at pillow dams in Sichuan province, China. The developed model was trained, validated, and tested, with the Hybrid model demonstrating superior performance. The results showed that the DDTL models had high classification accuracies, surpassing Convolutional identification techniques for sub-surface cracks. Consequently, this study suggests that automatic image classification techniques can effectively identify and localize structural defects in concrete dams. This is an innovative approach to predicting normal borehole images and crack recognition using CCTV borehole images.