Failure Risk Assessment Of Electro-mechanical Equipment In The Intelligent Operation And Maintenance Phase Of Urban Tunnels

Urban tunnels represent a fundamental aspect of contemporary city infrastructure,with their safe operation constituting a prerequisite for the efficient functioning of urban transportation systems and the preservation of human lives and property.A particular area of concern pertains to the possibility of electro-mechanical equipment malfunction,whose malfunction may hinder normal tunnel operation,jeopardize traffic safety,and compromise human safety and property protection.The impact of equipment failure on tunnel operational efficiency and service quality may pose severe risks to public safety.Thus,the evaluation of equipment failure risks during the intelligent operation and maintenance stages of urban tunnels assumes substantial theoretical and practical significance.The objective of this study is to investigate the risk of mechanical and electrical equipment failures and develop prevention strategies using complex network theory.Specifically,the work structure of the mechanical and electrical system in the urban tunnel maintenance phase and the various risk sources of equipment failures contained therein are comprehensively identified using the WBS-RBS method.The main research objects of this study are 19 mechanical and electrical equipment nodes and 17 equipment failure risk nodes.Next,data on the number of mechanical and electrical equipment failure accidents in urban tunnels are collected through network data crawling and literature research,and the probability of mutual triggering of equipment failure risk factors is assessed using the focus group discussion method.Based on the quantitative relationships between the main research objects,a 2-mode complex network model that includes mechanical and electrical equipment and various failure risk elements is constructed.The global and local parameters of the network,such as network structure,connectivity,and node clustering coefficient,are analyzed.Based on this model,a comprehensive evaluation method of node influence based on the global structure and local features of the network is designed.By coupling the results of these two parts of the calculation,impact assessment of various types of mechanical and electrical equipment failure risks can be achieved.Finally,data discretization is used to divide the impact of each equipment failure risk into six levels.Based on the results of risk impact evaluation and co-occurrence frequency,risk prevention strategies for different types of mechanical and electrical equipment failures are developed.In this study,a novel approach is presented for evaluating the impact of equipment failure risk through the construction of a two-mode complex network with directional weighting,termed "equipment-equipment failure risk".This approach enables an exploration of the interdependencies between different types of electro-mechanical equipment and their corresponding subordinate equipment failure risks from the complex network perspective.The incorporation of K-core analysis and node importance assessment methods of complex networks in risk assessment research allows for a multi-faceted evaluation of equipment failure risk at both macro and micro levels.This expands the applicability of complex network theory and facilitates the quantitative assessment of various equipment failure risks,underscoring its important theoretical and practical implications.