Image classification for subsurface crack identification in concrete dam based on borehole CCTV images using deep dense hybrid model
发布时间：2025-05-28
点击次数：
发表刊物：Stochastic Environmental Research and Risk Assessment
关键字：Borehole CCTV · Subsurface crack · Image classification · Stack ensemble · Water resources and predictive modelling
摘要：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.
合写作者：Saif Ur Rehman Khan, Yulong Luo, Bin Zhang, Wei Zhou
第一作者：Qianwei Dai
论文类型：期刊论文
通讯作者：Muhammad Ishfaque, Yi Lei
是否译文：否
收录刊物：SCI