###### Abstract
[13] developed a finite element model of a simply supported bridge that incorporated factors such as temperature, vehicle speed, and pavement roughness. The vehicle acceleration data generated by the model were analyzed in the frequency domain and were subsequently processed using a neural network architecture to evaluate the bridge's structural health. Machine learning-based indirect detection methods show great potential in bridge damage identification. However, their application still faces challenges, such as the need for large amounts of training data and, sometimes, manual classification or data labeling. Moreover, machine learning-based indirect detection methods often face challenges in adapting to complex real-world operational conditions, including pavement roughness, temperature fluctuations, and unpredictable traffic patterns. In recent years, signal processing-based indirect detection techniques, including wavelet transform (WT) and empirical mode decomposition (EMD), have attracted growing interest due to their simplicity and ease of implementation compared with machine learning-based approaches. [PERSON] et al. [14] introduced a damage detection method that extracts the intrinsic mode function (IMF) associated with the speed component of the response recorded from a passing vehicle, utilizing the empirical mode decomposition (EMD) technique. The method's effectiveness was further validated through numerical simulations. [PERSON] et al. [15] investigated the selection of the optimal continuous wavelet transform parameters combined with Shannon's entropy to recognize the location and extent of damage in bridge structures. The entropy of the wavelet function was measured at different scales, and the wavelet function corresponding to the scale with the lowest entropy had the highest sensitivity to damage. The fidelity of the proposed method in identifying bridge damage was investigated using numerical simulation. [PERSON] et al. [16] developed a comprehensive and systematic methodological framework for signal processing, combining wavelet analysis with ARMA modeling, applied to a three-span reinforced concrete arch bridge. The approach focused on extracting essential information from non-stationary response data to support structural interpretation, identification, and modeling. This methodology aims to contribute to the development of a robust and effective structural health monitoring (SHM) platform for critical aging infrastructure. A case study was conducted to validate the effectiveness of the proposed method. [PERSON] et al. [17] proposed a novel framework for bridge damage identification that employs continuous wavelet analysis on acceleration data collected from two sensors mounted on a vehicle crossing the bridge. In recent years, scholars have also explored the integrated application of various signal processing techniques to improve the bridge damage detection accuracy further. [PERSON] et al. [18] proposed a new method for tracking the evolution of structural damage in beams. The method first denoises the response signal using wavelet thresholding. Then, variable modal decomposition is introduced to adaptively decompose the denoised response signal into multiple single components. Finally, the proposed wavelet total energy change (WTEC) metric is utilized in order to locate the damage in the beam structure. The validity and accuracy of the method were verified using numerical simulations and a 10 m long steel bridge. [PERSON] et al. [19] utilized VMD to analyze the acceleration signals obtained from sensors mounted on a prestressed concrete bridge. Through this approach, the inherent frequencies of the structure were effectively identified, demonstrating the capability of VMD in modal parameter extraction for real-world civil structures. Although [PERSON] et al. [20] proposed the ensemble empirical mode decomposition (EEMD) method, which has demonstrated notable advantages in signal processing, it still faces some challenges in practical applications.