## 2 Related Works  
### Pavement Distress Classification  
Recently, the swift progress of intelligent transportation systems and advancement in autonomous driving technologies have drawn significant attention to the research on pavement damage detection and classification. Pavement damage not only affects driving safety but also accelerates vehicle wear and tear. Traditional manual inspection approaches are both time-consuming and labor-intensive, rendering them impractical for large-scale applications. As a result, automated detection techniques utilizing computer vision and deep learning have emerged as prominent research directions. The integration of deep learning, particularly CNNs, has notably enhanced the accuracy of pavement damage classification. For instance, [PERSON] et al. [18] developed a detection method based on deep CNNs, using large-scale datasets to train models capable of automatically identifying various damage types, including cracks and potholes. [PERSON] et al. [19] investigated the use of U-Net-based semantic segmentation networks for pavement damage detection. Utilizing fully convolutional networks (FCNs [20; 21; 22]), they achieved pixel-level classification, enabling not only the detection of damage but also the precise localization of the damaged areas. With the development of technologies like multimodal data fusion, [PERSON] et al. [23] proposed a multi-sensor fusion approach for road damage detection, which integrates RGB images and data from multispectral imaging sensors. By leveraging depth information from multispectral sensors to supplement incomplete visual data, they achieved notable improvements in both detection accuracy and model robustness. This multi-sensor fusion approach enhances the model's adaptability to complex environments while ensuring more accurate and reliable detection outcomes. [PERSON] et al. [24] explored the combination of thermal imaging with traditional visible light images to detect sub-surface pavement damage. They discovered that fusing multispectral data allows for the earlier detection of potential structural issues in the pavement. In the realm of supervised learning and multiple-instance learning, [PERSON] et al. [25] proposed an ultra-weakly supervised visual Transformer named PicT for pavement damage classification based on the Swin Transformer [26]. They developed a new pavement image classification Transformer (PicT) for this task. To better leverage the discriminative information at the patch level in pavement images, they proposed a patch label teacher model. In addition to computer vision-based detection approaches, [PERSON] et al. [27] proposed a linear viscoelastic (LVE) method for simulating pavement responses under moving loads. This method is based on the multi-layered elastic theory (MLET) combined with the elastic-viscoelastic correspondence principle. By employing a numerical inverse Laplace transform, the model enables the computation of time-dependent stress-strain responses and incorporates a time-collocation interpolation scheme to improve computational efficiency. [PERSON] et al. [28] developed a comprehensive mechanistic framework that integrates both elastic and viscoelastic theories for the in situ performance evaluation of asphalt pavements. The study emphasized the impact of material modeling assumptions (elastic vs. viscoelastic) on strain prediction and subsequent maintenance decision-making. Based on a combination of Falling Weight Deflectometer (FWD) and Ground-Penetrating Radar (GPR) data, along with core sample testing, the authors constructed master curves using the Huet-Sayegh rheological model and performed dynamic response simulations using the ViscoRoute software. These approaches collectively offer various solutions to the problem of pavement damage classification from different perspectives. The model generates pseudo-labels for image patches dynamically in each iteration, enabling the weakly supervised learning of discriminative patch features through patch label inference.