###### Abstract
The vehicle scanning method has garnered widespread attention, and much research has been conducted in bridge health monitoring in recent years. Researchers have primarily focused on vehicle-bridge coupled vibration analysis, theoretical model construction for vehicle scanning methods, and the application of signal processing techniques. [PERSON] et al. [6] were the first to propose a method for extracting bridge modal parameters based on the dynamic response of moving vehicles. Their theoretical analysis and numerical simulations confirmed that vehicle vibration signals encapsulate the modal characteristics of the bridge. Subsequently, as an initial attempt, [PERSON] and [PERSON] [7] investigated the frequency of a simply supported bridge by theoretically and experimentally analyzing the dynamic response of a moving test vehicle. The practicality of the vehicle scanning method was subsequently confirmed through field tests on real bridges. Since then, many scholars have used the vehicle scanning method to detect bridge girder damage. The modal parameter method is an important approach in bridge damage detection, which evaluates the health of a bridge based on its structural modal parameters (such as natural frequencies, damping characteristics, mode shapes, etc.). From the perspective of structural dynamic characteristics, frequency, as a holistic parameter, has low sensitivity for detecting local damage. Furthermore, the complexity of real-world structural damping mechanisms makes accurate estimation of the damping ratio challenging; as a result, damage identification methods based on its variation are seldom utilized [8]. In contrast, mode shapes can more effectively locate damage because it can cause abnormal changes in the mode shape at corresponding locations, such as local peak values or abrupt slope changes. Structural damage can be detected and localized by monitoring variations in a bridge's modal parameters in response to external loading. In recent years, applications of the modal parameter method in vehicle scanning have received ample attention and extensive research has been conducted in this area. [PERSON] et al. [9] proposed a general methodology for identifying the material parameters of concrete bridge structures through a reverse analysis approach. The procedure was validated using multiple pseudo-experimental case studies, encompassing both conceptual frameworks and practical models of concrete bridges. [PERSON] et al. [10] developed an inverse filtering technique to extract bridge characteristics from acceleration signals captured by smartphones mounted on passing vehicles. Their study demonstrated that the inverse filtering method holds considerable promise for accurately identifying the fundamental frequencies of bridges. [PERSON] and [PERSON] [11] proposed a bridge damage identification method based on modal parameters that detects changes in these parameters by analyzing the dynamic response of vehicles crossing the bridge to determine both the location and severity of the damage. [PERSON] et al. [12] analyzed the dynamic response characteristics as vehicles passed over a real bridge and proposed a bridge damage identification method based on modal parameter changes. Despite significant progress being made over the past two decades, bridge detection methods based on modal parameters still face several challenges in bridge damage detection, such as efficiently and accurately extracting modal parameters and eliminating interference from road roughness and environmental noise. In contrast to modal parameter-based approaches, non-modal parameter methods operate independently of modal parameters. In recent years, significant progress has been made in applying machine learning techniques to non-modal parameter methods. By training on large datasets, machine learning algorithms can extract complex patterns and features, enabling the automatic detection and identification of bridge damage. [PERSON] et al.