## 1 Introduction  
The growing trend of adopting the TBM tunnel excavation method instead of the traditional drilling and blasting approach has become increasingly apparent in various industries, including water conservation, transportation, and mining. When constructing tunnels and underground engineering projects, risks are influenced by objective factors such as geological conditions and design requirements, as well as subjective factors like construction technology expertise, disaster detection capabilities, and site organization and coordination abilities. These two sets of factors have a mutual impact on each other's levels. Due to the complex risks associated with TBM construction, there are multiple risk indicators that need identification; however, the evaluation results for each individual indicator often vary. Therefore, it is neither reasonable nor practical to solely rely on a highest-principle approach for grading evaluations. Consequently, this issue falls under the category of a typical comprehensive evaluation problem [1]. To enhance the scientific robustness of risk assessment in tunnel or underground construction projects, researchers have proposed various methodologies, including the application of techniques such as the Monte Carlo method, artificial neural network method, analytic hierarchy process, entropy weight method, and risk assessment matrix method. [PERSON] [2] has developed an improved variable weight model for the comprehensive evaluation of shield tunnel structural health. The model employs variable weight theory to replace traditional fixed weights, thereby reflecting the impact of index value fluctuationson index weights; [PERSON] [3] utilizes preference ratio and anti-entropy weight methods to analyze the weight of each index factor. Additionally, considering the uncertainty and fuzziness associated with landslide risk factors in diversion tunnel construction, an entropy theory-extension cloud model-based evaluation system is established for assessing diversion tunnel landslide risks; [PERSON] [4] achieves more accurate and effective safety risk evaluations of existing tunnels under shield underpasses by establishing a relatively perfect safety risk evaluation system and standard while introducing evidence theory that can effectively integrate uncertain information. Furthermore, fuzzy Bayesian networks are combined to build a safety evaluation model for existing tunnels under shield underpasses; [PERSON] [5] uses an analytic hierarchy process to calculate weights for all risk factors in a risk assessment index system and constructs a fuzzy relationship matrix combined with membership vectors obtained through the expert evaluation method. A nonlinear operator is introduced for comprehensive analysis of the risk weights and fuzzy relation matrix, resulting in a new TBM construction risk assessment model based on nonlinear FAHP; [PERSON] [6] proposes a security analysis method based on a fuzzy Bayesian network that takes into account judgment ability level, reliability level auxiliary pipe revealing data reliability when determining basic events' fuzzy probability occurrence along with expert confidence indexes; [PERSON] [7] developed a comprehensive model that combines a fuzzy analytic hierarchy process (FAHP) and set pair analysis (SPA) to assess safety risks in long highway operation tunnels. This integration successfully merges subjective weighting with objective evaluation, resulting in improved accuracy of these assessments. The construction of the tunnel project is not only large-scale, very deep, and long-distance, but in this kind of tunnel project, the site geological conditions, natural climate, and hydrological conditions are more complex, and in the construction process, under the joint action of various risk factors, the risk of accidents is very high.