## 5 Conclusions  
A new method of bridge damage identification combining CNN and LSTM is proposed. The proposed method uses LSTM to extract the time-series features of the acceleration data, which are merged with the high-dimensional features of the acceleration data extracted by CNN to realize bridge damage localization and severity prediction. A finite element model was employed to simulate the damage of the hangers of a large-span suspension bridge, and the acceleration dataset was obtained by time-history response analysis to verify the effectiveness and accuracy of the proposed model. The following work was carried out:  
1. A database containing 8670 damage samples was constructed and divided into a training set, validation set, and test set. Afterward, the training set and the validation set were used to train and select the proposed CNN-LSTM model. For the entire test set, the accuracy of the damage localization of hangers reached 94.00%, and the ARIE of the damage severity prediction was just 8.00%.
2. To study the noise immunity of the proposed method, noise with different signal-to-noise ratios was added to the samples in the database, and then the CNN-LSTM model was retrained, and the test set after adding noise was predicted. The results show that when the SNR is 3.33, the accuracy of damage localization reached 67.06%, and the ARIE of the damage severity identification was 31%.
3. The performance improvement of the CNN-LSTM model was investigated. The damage recognition performances of the CNN and CNN-LSTM models were compared and analyzed. The results show that the accuracy of damage localization based on CNN-LSTM increased by 8.13%, and the ARIE of the damage severity prediction decreased by 5.20% compared with CNN.  
From the above results, it could be concluded that the proposed method can accurately realize the identification of different damage locations and degrees of the target hangers.  
Figure 13: Performance comparison: (**a**) Damage localization; (**b**) Damage severity prediction.  
However, this method may possess a poorer recognition effect on the hangers with minor damage. Therefore, it is indispensable to improve the recognition effect of minor damages in the future. In addition, the main factor that affects the accuracy of the model is the similarity between the training set and the actual situation. As a result, more data under different scenarios will be collected in the future, which is expected to further improve the accuracy of the model.  
Data curation, Q.T.; Formal analysis, Q.T.; Funding acquisition, J.X.; Methodology, L.F.; Software, L.F.; Supervision, J.X.; Validation, P.G.; Writing--original draft, P.G.; Writing--review and editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51978111, 51908094), the Technology Innovation and Application Demonstration Project of Chongqing (cstc2019 jscxgksbX0047), the Science and Technology Support Project of Guizhou Science and Technology Department (Qianke Cooperation Support [2018] 2154).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors sincerely thank the anonymous reviewers for their thorough reviews and constructive comments. The authors also thank the editors for their selfless contributions in the manuscript processing.  
The authors declare no conflict of interest.