## 5 Conclusions  
Bridge damage identification generally encompasses several essential steps: damage detection, localization, classification, and severity assessment and overall structural performance prediction. This study primarily concentrates on two fundamental aspects--damage detection (determining whether damage exists) and damage localization (identifying the location of damage)--while also exploring the evaluation of damage severity and the method's effectiveness in scenarios involving multiple damage sites. This study introduces a damage identification index that integrates contact point acceleration responses with continuous wavelet transform analysis, aiming to achieve effective and accurate bridge damage detection. The study first derived the equations for the contact point response of the vehicle-bridge coupled vibration system. It validated the feasibility of using the damage index in identifying bridge damage using the contact point response. Based on a theoretical analysis and numerical simulations, the following conclusions can be drawn:  
Figure 10: Effect of pavement roughness: (**a**) the contact point response spectrum; (**b**) contact residual response under A-grade road surface roughness. Figure 9: Variational modal decomposition–continuous wavelet transform with different numbers of spans. 1. The variational mode decomposition technique helps separate different frequency components in the contact point acceleration response, eliminating modal aliasing and spurious frequency issues. This improves the accuracy and stability of damage identification. 2. Studies show that within a certain range, the effectiveness of damage identification improves with increased vehicle speed. However, bridge deck roughness may affect excessively high speeds, interfering with identification results. 3. Using the continuous wavelet transform to process the contact point acceleration response can extract bridge damage features effectively. The effective scale factor range of the continuous wavelet transform coefficients extracted by this method is broad, and adjusting the scale factor can further improve the accuracy of damage location identification. 4. The proposed variational modal decomposition-continuous wavelet transform method is not only applicable for the identification of damage in simply supported beams but also for the identification of damage in continuous beams. 5. Even when considering road surface roughness, the proposed damage identification method can still accurately identify damage locations, demonstrating its potential for practical application under complex conditions. It is important to note that this research focuses on immediate damage detection and localization and does not extend to long-term predictions of structural performance degradation or remaining service life based on the identification outcomes. However, this research direction has substantial engineering significance and will form a central part of future work. Upcoming studies will aim to integrate detection data with machine learning techniques to develop predictive models for bridge performance degradation, thereby enabling a more comprehensive approach to structural health assessment and life-cycle prediction. Additionally, current research on the vehicle scanning method primarily focuses on theoretical derivation and numerical simulation. More field bridge tests should be conducted to promote the practical application of the vehicle scanning method. This will validate the effectiveness of the theoretical research and provide practical data support for the improvement and optimization of the method, thereby promoting the widespread application of the vehicle scanning method in bridge health diagnostics. Recent applications of and investigations into the vehicle scanning method are predominantly concentrated on conventional medium- and short-span bridges, such as simply supported and continuous girder bridges.