# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 1 Introduction  
Urban dense cable channels perform important transmission tasks such as electricity and communication, and their operational safety is directly related to the normal operation of the city and social stability [1; 2; 3; 4]. However, due to the complexity of the internal environment of the cable channel, the changeable external environment, and the interference of human factors, there are many risks in its operation [5; 6; 7]. Therefore, it is of great significance to carry out research on the operational risk assessment of urban dense cable channels. In recent years, researchers have carried out many studies on the operational risk assessment of urban dense cable corridors, and they have constructed a system of operational risk assessment indexes for urban dense cable corridors by comprehensively considering the internal environment of the cable corridors, the external environment, the state of the equipment, human factors, etc. These indicator systems usually contain multiple primary and secondary indicators, such as temperature, humidity, accumulation of materials, etc., in the internal environment; geological conditions, climatic conditions, etc., in the external environment; equipment aging, failure rate, etc., in the equipment status; and human factors, such as operating standards, emergency plans, etc. [8; 9; 10]. Ref. [11] divides cable lines into five parts according to inspection objectives: the cable channel, body, intermediate joints, termination, and grounding system, and it invokes the failure hazard index modelto calculate the failure frequency of each part, which is based on the risk assessment of the inspection cycle optimization method. This can reasonably formulate the inspection program, but it does not consider the impact of the cable channel and the risk factors of the external environment on the inspection program. Ref. [12] proposes a comprehensive assessment method for cable channels based on improved ANFIS, which marks cable channels into different risk levels through hierarchical clustering to provide support for operation and maintenance personnel. However, it needs to integrate the Internet of Things and edge computing technology, which requires high computer performance and is more complex to calculate. With the development of artificial intelligence technology, machine learning methods are gradually applied to assess risks. For example, algorithms such as decision trees, random forests, and support machines are utilized to mine the risk patterns in the historical data of cable channels [13; 14; 15]. Ref. [16] proposed a study of an integrated monitoring system for cable duct networks based on random forests, verifying the effectiveness of the described method in the assessment of the operational status of cable duct networks through experimental analysis. Ref. [17] proposed a reliability analysis method for high-voltage cables by combining Bayesian inference with the Weibull proportional risk model. Ref. [18] proposed a method for identifying the developmental stages of air gap discharge in XLPE cables based on the improved K-nearest neighbors algorithm. However, machine learning algorithms require a high quality and quantity of data and exhibit poor model interpretability, which need to be weighed against their performance and interpretability in practical applications. Hierarchical analysis is a method that decomposes a complex problem into multiple levels, and it determines the relative importance of each factor by establishing a hierarchical model, constructing a judgment matrix, calculating a weight vector, and performing a consistency test, among other steps [19; 20; 21; 22]. In cable channel operation risk assessment, risk factors can be categorized into a target layer, a criterion layer, and an indicator layer.