## 3 Results
### Grad-CAM Visualization Results  
Figure 9 displays the Grad-CAM heat maps of the eight network architectures on the correctly classified representative samples of each category. This visualization highlights the differences in the models' regions of interest for the images of the four categories of distress samples. The models were selected for the same reasons as when generating the confusion matrix. All models selected the final convolutional layer for the Grad-CAM operation. This is because the last convolutional layer is closest to the output layer in the network model, has a higher level of feature representation, and can more accurately reflect the distribution of the model's attention to the image before classification prediction.  
Figure 9: Grad-CAM heat maps of correctly categorized representative samples of each category on 8 network architectures. For each distress, three representative samples were selected. The heat map displays the distribution of the model’s attention in the distress images, highlighting the differences in the regions of focus for the different categories. The model’s attention is indicated by warm colors, while cool colors indicate regions that have little impact on the classification prediction.  
The attention regions of the models correlate with the damage characteristics, and there is some similarity in the attention of different models for the same sample, but there are also significant individual differences, as shown in Figure 9. For interlayer loosening, interlayer debonding, and interlayer water seepage, AlexNet_64, SqueezeNet1\(1\)64, and the four models of the VGG architecture showed minimal areas of attention. In contrast, InceptionV3_64, DenseNet169_16, MobileNetV3_16, and the five models of the ResNet architecture had larger areas of attention. For structural loosening, most models have a larger region of interest, particularly EfficientNetB2_64, except for SqueezeNet1\(1\)64.  
Some typical and representative misclassified samples were selected to further explore the possible reasons for misclassification via these models by generating Grad-CAM heat maps, as shown in Figure 10. Obviously, when classifying these distress samples, the attention of some models seems to be biased towards other regions, which is different from that when classifying correctly. This shift in attention may be an important reason for the misclassification. From the prediction categories and confidence values, sample-13 and sample-14 are misclassified as interlayer water seepage and interlayer debonding, respectively, by most models. Sample-15 and sample-16 were both predicted as interlayer loosening, and the confidence values of the incorrect predictions were relatively high. Most models incorrectly predicted Sample-17 as interlayer water seepage and sample-18 as interlayer loosening. Sample-19 and sample-20 were also incorrectly predicted by the majority of the models, and the other three distresses were all possible categories of incorrect predictions. These results align with the confusion matrix shown in Figure 8.  
Figure 10: Grad-CAM heat map of some typical and representative misclassified samples on eight network architectures. The first column displays the images and true labels of the distress samples input to the model. The following columns show the Grad-CAM heat maps for each model for each sample. Below each image, the top two prediction categories and confidence values of the model’s prediction results are also labeled. Correctly classified results are labeled in green, and misclassified results are labeled in orange.