## 5 Conclusions  
This paper proposes a visible-light image-based damage assessment method for girder bridges using image preprocessing-related methods to process the original image and then utilizing a change detection algorithm to extract the damaged area. At the same time, the girder bridge is decomposed into piers and bridge decks, its functional characteristics are analyzed, and the residual bearing capacity and the number of traffic paths associated with its function are proposed. On this basis, the conversion relationships between the image features and the residual load carrying capacity of the bridge, and the image features and the number of traffic paths of the bridge, are established, and a coupling calculation method is designed to synthesize the two damage features and calculate the target damage level of the girder bridge after the strike. Consequently, damage level assessment rules are formulated, providing a feasible method for the damage assessment of girder bridges. Finally, a test simulation is carried out to verify the feasibility and precision of the model, and the simulation results show that the algorithm can effectively evaluate the damage level of the girder bridge target. Compared with the current studies on the destructive effects of bridge munitions strikes, our method has several main advantages: Accuracy: the residual bearing capacity feature and the number of passable lanes feature that respond to the function of the bridge were designed and calculated from the image, which is more intuitive and detailed compared to the destruction features in [11; 15]. Feature extraction: the residual bearing capacity feature of the bridge after the strike was extracted by using images, and the number  
Figure 8: Schemes follow the same formatting: (**a**) Pre-damage target image. (**b**) Simulated post-damage target image. (**c**) Change detection results. (**d**) Target extraction results. (**e**) Target area crater detection. (**f**) Visualization of crater locations. (**g**) Results of residual bridge abutment load rating. of passable lanes feature was extracted based on the principle of image logic operation, which ensures that the damage features accurately describe the function of the bridge and makes the damage assessment more scientific. Data scale: Compared with the machine learning-based damage assessment method, it does not need a large number of samples for model training, and the data volume requirement is low while ensuring the reliability of the results. Convenience: The method does not require high image quality and only requires long-distance shooting with a camera-equipped UAV to acquire images of bridge targets. This improves the convenience and efficiency of data acquisition. This work also has some limitations. First, different image preprocessing algorithms may have different effects on the results of change detection, and further research on excellent image preprocessing algorithms is a hot topic in current research. Secondly, although this method can be used for war damage assessment of girder bridges, it is mainly aimed at the traffic performance of bridges, and further in-depth study of the functional characteristics of bridges, combined with mechanical calculations, should be undertaken in order to make our feature extraction method more accurate. Conceptualization, X.C. ; methodology, X.C. and W.W.; software, X.C. ; validation, H.Z., Q.X. and W.W.; formal analysis, X.C. ; investigation, X.C. ; resources, H.Z. and Q.X. ; data curation, X.C. ; writing--original draft preparation, X.C. ; writing--review and editing, H.Z., Q.X. and W.W.; visualization, X.C. ; supervision, H.Z., Q.X. and W.W.; project administration, H.Z. ; funding acquisition, H.Z. and Q.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. All data generated or analyzed during this study are included in this paper or are available from the corresponding authors on reasonable request. The authors declare no conflicts of interest.