## 4 Results and Analysis
### Analysis of Factors Influencing Tunnel Service State  
According to the LGBM algorithm, the factors influencing the service state of the tunnel were ranked using the split method (making use of the number of splits). The total number of splits of the tree model was sorted based on the features, providing further help for analyzing the impact of a variable on the prediction results. Shapley additive explanations (SHAP), a model interpretation package developed in Python, was utilized to explain the machine learning models using game theory methods and determine the contribution of each feature. To further enhance the interpretability of the model, we also incorporated SHAP in validating and analyzing the ranking of feature importance. This paper also efficiently explains the ranking of the factors influencing the operational performance of tunnels from the perspective of the intrinsic evolutionary mechanism of tunnel performance. Figure 5: Confusion matrices and Receiver Operating Characteristic (ROC) curves for LGBM models: (**a**) structural section; (**b**) civil section; and (**c**) non-structural section. Figure 6 shows the feature importance ranking and SHAP ranking for the three types of elements: structural, civil, and non-structural sections. The results show that several factors have a considerable influence on the service states of tunnel sections; these include service time (F5), tunnel length and width (F10 and F17), longitude and latitude (F4 and F3), the minimum vertical clearance (F16), the annual average daily traffic (F8), and annual average daily truck traffic (F9). However, the importance ranking orders of these features vary for different sections. The influential factors can broadly be classified into three categories: (1) natural factors, such as the latitude and longitude of the tunnel (F3 and F4), service time (F5), etc. ; (2) human factors related to traffic, such as the annual average daily traffic (F8), annual average daily truck traffic (F9), etc. ; and (3) tunnel attributes, such as tunnel length (F10) and width (F17) and vertical clearance (F16). Figure 6: The feature importance rankings and SHAP rankings (top 20) for the (**a**) structural; (**b**) civil; and (**c**) non-structural sections. Figure 6 shows that the service time of the tunnel (F5) is one of the top two factors influencing the service state ranking of the three types of elements. This finding is consistent with the results of field investigations and experimental studies carried out by several researchers regarding the deterioration of tunnel service states [23; 24; 25]. In addition to service time, the geographic location of the tunnel (latitude and longitude) (F3 and F4) is also an important factor that affects the service state of a tunnel. The geographic location of a tunnel is closely tied to its temperature and climate. The literature contains many studies on how the natural environment affects the service states of tunnel structures. For instance, [PERSON] et al. [26] utilized intelligent temperature recorders to examine the temperature fields of tunnels within a cold region. The findings established a correlation between the deterioration of the lining concrete and the number of unsaturated freeze-thaw cycles it endured. The ranking shows that latitude (F3) has a more significant impact on the service performance of the three sections than longitude (F4), likely due to the more direct influence of latitude on the temperature of the service environment. It is worth noting that the service performance of tunnel elements is significantly impacted by human-related factors associated with traffic. These factors not only significantly affect the service states of civil sections such as pavements, guardrails, etc., that directly interact with vehicles but are also closely related to the service performance of structural sections that do not have direct contact with vehicles.