Comparative Analysis of VGG16 Transfer Learning Fine-Tuning Strategies for Automated Concrete Crack Classification - Adwinof Akmal Juantoro; Sugiyanto Sugiyanto (Building of Informatics, Technology and Science (BITS), 2026)
Identifying cracks in concrete structures is critical for structural health monitoring, as undetected cracks can lead to catastrophic infrastructure failure. Conventional manual inspections are labour-intensive, subjective, and costly, necessitating automated solutions capable of consistent and scalable deployment. This paper presents a systematic comparative study of four VGG16 transfer learning strategies for automated binary classification of concrete surface cracks. VGG16 was selected for its proven effectiveness in binary image classification tasks, well-established pre-trained feature representations from ImageNet, and low trainable parameter count that reduces overfitting risk on domain-specific datasets. A dataset of 40,000 concrete surface photographs was utilised, divided 80:20 for training and validation. Four training configurations were evaluated: Baseline CNN, Full Freeze, Partial Fine-Tuning, and Full Fine-Tuning, all trained using the Adam optimiser (learning rate 0.001), binary cross-entropy loss, and early stopping. Partial Fine-Tuning achieved the highest accuracy at 99.90%, followed by Full Freeze (99.84%) and Baseline CNN (99.69%). Full Fine-Tuning collapsed to 50.00% due to catastrophic forgetting. The best-performing Partial Fine-Tuning configuration achieved an AUC of 0.9998, precision of 0.9990, recall of 0.9990, and F1-score of 0.9990, with only 15 misclassifications out of 8,000 validation samples. These results confirm that Partial Fine-Tuning is the recommended strategy for concrete crack classification in structural health monitoring application.