## 5 Conclusions  
In this paper, machine learning models based on the NTI database of the Federal Highway Administration (FHWA) were developed. According to their functions, tunnel elements were classified into three categories, namely, structural, civil, and non-structural sections. The service states of these elements were then classified into different ratings. Machine learning models for predicting the service states of tunnel elements were developed using LGBM, KNN, SVM, MLP, DT, and RF algorithms. Furthermore, an analysis of the importance of features influencing tunnel service state was conducted using the LGBM model. The main findings of this study are as follows. (1) After analyzing the evaluation metrics of accuracy, precision, recall, and F1-score, it was found that LGBM performed the best, while MLP performed the worst. Ensemble  
Figure 8: Illustration of structural damage accelerating long-term performance degradation. Figure 7: Accident types versus traffic accidents [27]. learning algorithms such as LGBM and RF performed better in terms of addressing the issue of the imbalanced classification of tunnel elements' service states. Moreover, the LGBM algorithm had an advantage over the other algorithms in conducting feature importance analysis based on a large quantity of data as it can directly handle categorical features without the need for one-hot encoding. (2) The machine learning models established using the LGBM algorithm exhibited prediction accuracies of 90.9%, 96.4%, and 77.3% for the structural, civil, and non-structural sections, respectively. These results indicate that the LGBM models possess strong generalization ability. (3) The feature importance analysis conducted on the LGBM model revealed that service time, tunnel length, latitude and longitude, the minimum vertical clearance over the tunnel roadway, AADT, AADTT, and tunnel width are the important factors affecting the service states of various type of tunnel elements. In addition to the environmental features and tunnel attributes of tunnels, human factors such as traffic volume have a significant impact on the structural section of tunnels. This study also shows that the sudden effects of impacts, explosions, and fire, constituting secondary disasters caused by traffic accidents, have the potential to lead to long-term deterioration of tunnel structure performance. Therefore, further research should be carried out to analyze the coupling mechanisms of such sudden effects regarding the long-term service performance of a tunnel, including investigations on the performance degradation of pre-damaged tunnel structures in aggressive underground environments, and, at a microscopic level, the transport mechanisms of erosive ions in pre-cracked concrete as well as the uneven corrosion characteristics of rebar in pre-damaged concrete cover. (4) The machine learning model used in this study is based on survey data collected in the United States. Therefore, further data are needed to validate the applicability of the conclusions to the operational conditions of tunnels in China. It would be of great significance for various parties, such as transportation management agencies, tunnel owners, and tunnel inspection units, to collaborate to establish a tunnel operational status database specific to China. With a large quantity of high-quality field data, tunnel operational performance patterns in China can be more clearly revealed at a macro level via data-driven models. Moreover, for the practice of tunnel inspection, more detailed data related to traffic volume should be collected, such as the rates and types of traffic accidents. With more parameters considered in the predictive model employed, its accuracy can be further improved. [PERSON] was in charge of conceptualization, data processing and analysis, methodology, and making important revisions to the original draft. [PERSON] contributed to the data processing, model construction, and writing of the original draft.