## 1 Introduction  
Tunnels have become an integral part of the transportation network, connecting vast terrains across China and other countries. Various tunnels have become operational in the last few decades, and the emphasis in tunnel engineering research and practice has gradually shifted from the construction phase to the operation phase throughout a tunnel's entire life cycle. Tunnel infrastructures are usually designed to last for over 100 years, but they are subjected to a combination of complex loadings and aggressive environments, leading to long-term service performance degradation. Considering the massive initial construction investments required and maintenance difficulties encountered during the service lives of tunnels, the performance degradation of tunnels can create significant economic and social issues. Therefore, evaluating the service states of tunnels is critical to maintaining healthy service performance, ensuring the safety of tunnel structures, and allowing smart decisions to be made for interventions regarding maintenance and repair measures. Researchers have identified various factors affecting the long-term service states of tunnels and their significance. In-service inspections have shown that many tunnels suffer from critical issues such as lining cracking [1,2], water leakage [3,4], floor heave [5,6], etc. Moreover, tunnels built in coastal areas and the southwestern mountainous regions of China suffer from degradations caused by the erosion of chloride and sulfate ions existing in the surrounding soil and water [5]. [PERSON] et al. [7] investigated defects observed in 90 operational tunnels in China. It was found that the most common defects were cracks in the lining, water leakage, and human factors. A similar study conducted in Sweden revealed that the factors influencing the deterioration rate of tunnels include tunnel service time, traffic volume, the ventilation system employed, the waterproofing system employed, rock type, and groundwater chemistry [8]. [PERSON] et al. [9] conducted a case study on a metro shield tunnel in soft ground in Shanghai and proposed a comprehensive mathematical model for assessing the performance of tunnels. This mathematical model yields a Tunnel Serviceability Index (TSI) based on various measurable indicators obtained through statistical analysis. An analysis based on the TSI formula revealed that relative settlement, uneven settlement, convergent deformation, and water leakage significantly influenced the tunnel's condition, while cracks and spalling had a lesser impact. A tunnel's service state is influenced by numerous factors, which often have complex coupling effects. However, the aforementioned research has failed to account for the complex coupling effects of serviceability performance factors when evaluating tunnel serviceability due to the unavailability of required applied theory and standard procedures. In order to understand and study tunnels' service states, data-driven machine learning techniques have been developed, constituting powerful tools due to their ability to explore the implicit relationships among multiple factors. Various machine learning algorithms have been applied in research to assess tunnels' service states. [PERSON] et al. [10] employed the Long Short-Term Memory (LSTM) algorithm to extract data features from tunnel inspection data and accurately predict tunnel deformation. [PERSON] et al. [11] utilized data-driven machine learning techniques to detect potential geological changes and anomalies that may occur during tunnel construction. [PERSON] et al. [12] developed prediction models for tunnel deterioration using machine learning algorithms. However, the machine learning algorithms used in these studies still face challenges, such as an inability to directly process categorized features and obtain feature importance rankings, not being good enough to handle unbalanced categorization problems, having long computation times, and possessing insufficient accuracy and interpretability.