# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
## 5 Conclusions  
To enhance the predictive performance of a deep learning model, it is essential to reduce the loss value. Besides ensuring a highly accurate model with good generalization capabilities, achieving low loss values can also reduce the number of iterations needed, enhance stability, and prevent overfitting. In this study, we compared and analyzed the loss values of three deep learning models for identifying damage in aging bridges. The experimental results showed that BlendMask outperformed Mask R-CNN at a learning rate of 0.01. For the SWIN, learning occurred so quickly at this rate that the loss value trend  
Figure 11: Loss values for SWIN (learning rate: 0.0001).  
Figure 10: Loss values for BlendMask (learning rate: 0.0001).  
was not clearly observed. At a learning rate of 0.005, the SWIN exhibited both lower loss values and a faster learning speed. When the learning rate was set to 0.0001, it was possible to compare all three models, with both BlendMask and SWIN demonstrating decent loss values; however, SWIN excelled in learning speed. Overall, the SWIN demonstrated the best performance; its performance can be maximized by adjusting the learning rate. Using these experimental results, it is possible to find out which instance-segmentation model is optimal for identifying damage to old bridges and how parameter values such as learning rate affect the use of various models. Using these results, it is possible to apply the most optimal deep learning model for each type of bridge damage.  
However, in this study, it was impossible to compare loss values for various parameter values, and the amount of learning data was very small (about 4000), making a more objective comparative analysis difficult. Also, the number of images used for experiment and verification was about 200, so the results were slightly different for each experiment. Lastly, there are many more deep learning models besides the three representative ones studied here, and these should also be considered.  
In future research, we plan to investigate hyperparameter tuning to reduce loss values more effectively and to conduct rigorous experiments and verification of the results. Model combination algorithms should be investigated to further improve the accuracy of the proposed automated bridge-damage-identification model. Finally, we plan to develop an optimized mobile application capable of obtaining real-time images from bridges and identifying damage, thereby advancing the automation of bridge inspection and assessment tasks.  
Conceptualization, S.-W.C. and B.-K.K.; Software, S.-W.C. and S.-S.-H.; Validation, S.-W.C. and S.-S.H.; Resources, S.-W.C.; Data curation, S.-W.C.; Writing--original draft, S.-W.C.; Writing--review and editing, S.-W.C., S.-S.H. and B.-K.K.; Supervision, B.-K.K.; Project administration, B.-K.K.; Funding acquisition, B.-K.K. All authors have read and agreed to the published version of the manuscript.  
Research for this paper was carried out under the KICT Research Program (project no. 20230073-001, Development of DNA-based smart maintenance platform and application technologies for aging bridges) funded by the Ministry of Science and ICT.  
Not applicable.  
Div2K dataset--Super Resolution Benchmark Dataset (link: [[https://data.vision.ee.ethz.ch/cvl/DIV2K/](https://data.vision.ee.ethz.ch/cvl/DIV2K/)]([https://data.vision.ee.ethz.ch/cvl/DIV2K/](https://data.vision.ee.ethz.ch/cvl/DIV2K/)), accessed on 30 October 2022).  
The authors declare no conflict of interest.