## 2 Methodology
### Dataset Description
#### 2.1.2 Constructing Dataset
We obtain a dataset containing 31,640 samples using the flowchart shown in Figure 3. PCA dimensionality reduction was performed to visualize the distribution of the dataset, as shown in Figure 4. The categories of interlayer debonding, interlayer water seepage, and structural loosening have clear boundaries, particularly the most pronounced boundaries observed between structural loosening and the other categories. However, interlayer loosening partially overlaps with the sample distribution of the other three categories. Figure 2: Comparison of GPR B-scan images of four types of highway distresses and core samples. The distresses include interlayer debonding (**a**,**b**), interlayer water seepage (**c**,**d**), interlayer loosening (**e**,**f**), and structural loosening (**g**,**h**). The dataset was divided into training, validation, and test sets in a ratio of 6:1:3. Table 1 shows the number of samples in each category for each dataset, revealing a significant sample imbalance problem. In particular, the structural loosening category of distress has only 698 samples. Among the interlayer distresses, interlayer debonding generally occurs first; then, interlayer debonding develops into interlayer water seepage due to  
Figure 4: Three-dimensional distribution plot of the dataset after PCA dimensionality reduction. The plot visualizes the four categories of distress samples, and each point represents a distress sample (interlayer debonding—green, interlayer loosening—blue, interlayer water seepage—purple, and structure loosening—yellow) (**a**). The axes in the plot are the projections of the dataset on the new three principal components. For ease of observation, only the sample points of the interlayer loosening are hidden (**b**). Figure 3: Flowchart of constructing dataset. Using GPR to collect raw data on-site (**a**); processing raw data with processing methods such as dewow, adjusting the start time, compensating for energy decay, and so on (**b**); extracting the B-scan image with more prominent characteristics of the distress and segmenting every twenty meters of the corresponding highway of the B-scan image (**c**); handing over all the data to professionals for distress labeling work (**d**); obtaining images and labels of the four categories of distresses to build the dataset (**e**). rainwater infiltration and interlayer water seepage develops into interlayer loosening as the highway continues to be operated. Table 1 shows that interlayer debonding has the highest number of samples, followed by interlayer water seepage, and interlayer loosening has the lowest number of samples among the interlayer distress categories, which is consistent with the development process of highway distress. In addition, structural loosening, the most severe distress category, has the lowest number of samples. Therefore, the highway distress samples are significantly unbalanced. Although various methods have been proposed to deal with sample imbalance, such as sample augmentation [19] and loss function design [20], this paper concentrates on finding the optimal CNN-based model for highway ground-penetrating radar datasets, which exhibits sample imbalance.