# Fuzzy Synthetic Evaluation of the Long-Term Health of Tunnel Structures
## 1 Introduction  
Over the past two decades, China has observed the rapid development of highway and railway transportation systems, including many tunnels. In recent years, with the increase of the service time of these tunnels, various problems, such as lining cracks, water leakage, and even falling linings, have been widely observed and attracted attention from practicing engineers and academic researchers. The concepts of \"prevention\" and \"early discovery\" have been developed and gradually put into practice. Specifically, the long-term structural monitoring of tunnels that pass through complex and special environmental conditions has received additional attention in recent years. One example is the research on the application of structural health monitoring in Hangzhou Qiantang River Tunnel done by [PERSON] et al. [1]. [PERSON] et al. studied the structural health monitoring system for Nanjing Yangtze River Tunnel [2]. The stress of the secondary lining was selected as a long-term monitoring project and was successfully applied in Cang Ling Tunnel in Zhejiang province [3]. [PERSON] et al. successfully established a long-term health monitoring system for the Mo Tianling Extra-long Highway Tunnel [4]. Effective monitoring techniques were adopted to ensure the operation security of Xiamen Xiang'an Subsea Tunnel [5]. Located in Antwerp, Belgium, the Liefkenshoek tunnel was studied in terms of the structural health response to tidal fluctuations [6]. Due to their particular and complex engineering environment, [PERSON] et al. studied the long-term structural health monitoring of subsea tunnels [7]. A tunnel monitoring system was set up to monitor joint movements in the concrete tunnel lining of an existing London underground tunnel [8]. Structural monitoring is also used in a typical metro tunnel located in Rome, Italy [9]. With the development of sensors and other monitoring techniques, new monitoring methods are gradually applied to structural health monitoring of tunnels. [PERSON] et al. presented an automatic crack detection and classification methodology for subway tunnel safety monitoring [10]. The application of the fiber Bragg grating (FBG) sensing technology for safety monitoring during railway tunnel construction was researched [11]. Tunnel structural health monitoring can also be achieved by terrestrial laser scanning [12]. Since a tunnel is a coupled system of the surrounding rock and the supporting structure, the health status of the tunnel structure is influenced by the stability of the surrounding rock. Hence, the stability of the surrounding rock is of great importance. The stability of the surrounding rock is usually influenced by the lithology, the geological conditions, the initial geostress field conditions, the topography, the ground water conditions, etc. Therefore, there is enormous uncertainty about the stability of the surrounding rock. In addition, the structural health status is influenced by the shape and size of the tunnel section, the support type, and the construction method. Furthermore, the factors that influence the health of the tunnel structure interact with each other, together determining the long-term health status of the tunnel structure [3]. Thus, the tunnel structural health monitoring is a complex system. In the literature, intelligent methods, including neural networks [13; 14; 15], genetic algorithms [16; 17], fuzzy logic methods [18; 19; 20], Analytic Hierarchy Processes (AHP) [21; 22], etc., have been developed to deal with complex systems. Among these methods, the fuzzy logic method is of particular interest in monitoring structural or tunnel safety, which is commonly described by linguistic variables, such as safe, basically safe, potentially unsafe, and unsafe. In the fuzzy logic family, the fuzzy synthetic evaluation method is frequently used. The synthetic evaluation method is a general evaluation of phenomena affected by a variety of factors. If this evaluation process involves fuzzy factors, then it is called a fuzzy synthetic evaluation method.