A Novel Method for Risk Assessment of Cable Fires in Utility Tunnel

Abstract

Given the flammability of power cables and the high cost of utility tunnel construction, power cable fires cause serious economic losses and are associated with a negative social impact. In the study, a weighted fuzzy Petri net and an event tree are combined to propose a quantitative evaluation method to mitigate cable fire risks in a utility tunnel. First, cable fire risk factors are analyzed. Given the lack of utility tunnel cable fire historical data, fuzzy theory is used to calculate the failure probability of the primary event. Second, a weighted fuzzy Petri net is used for fuzzy reasoning, and an event tree is used to analyze all possible consequences. Subsequently, the numerical simulation method is used to quantify the loss from the cable fire and thereby quantify the risk of cable fire. Finally, the effect of different risk factors on a cable fire is analyzed to determine the main factors that affect cable fires. Simultaneously, the control ability of different control measures with respect to the fire is analyzed to determine key control measures. A case study of a utility tunnel cable cabin in Liupanshui in Guizhou is employed to validate the utility of the proposed method.

A Risk Analysis Method of Cable Fire in Utility Tunnel Based on a Bayesian Network

“…In the BN model, the slight change of the prior probability of each basic event (root node) may lead to the change of the fire probability of the cable in the utility tunnel. In order to quantify the degree of change in the BN model result caused by the basic event probability and identify in the critical basic events nodes of the BN model, the sensitivity analysis method is adopted to find out these critical basic events nodes [30]. In general, sensitivity analysis methods include Birnbaum importance measure (BIM), ratio of variation (RoV), and risk reduction worth (RRW) [31].…”

“…e new information represents the frequency of some basic events occurring within a certain time interval (such as one year) as evidence. e probability P updated by the new information can be obtained by (4) [30].…”

Analysis of the Risk Path of the Pipeline Corridor Based on System Dynamics

“…Risk evaluation: in the research on risk evaluation of integrated pipeline corridors, Canto-Perello et al proposed an expert system combining color-coding, Delphi, and hierarchical analysis methods to analyze the criticality and threat of integrated pipeline corridors, which was used to support the planning of safety policies for urban underground facilities [14]; Jang et al studied the risk of gas leakage and unknown ignition in integrated pipeline corridors due to gas explosions [4]; Wang et al constructed a risk evaluation model for integrated pipe corridor PPP projects, identified risk factors for utility based on a questionnaire survey, and then designed a risk evaluation index system and used an optimized fuzzy integrated rating method for risk evaluation [15]; He et al proposed a new fire risk assessment method within integrated pipe corridors in the absence of historical cable fire data, fuzzy theory was used to calculate the failure probability of the main events of cable fires, fuzzy inference was performed using a weighted fuzzy Petri net, and a numerical simulation method was used to quantify the losses caused by cable fires so as to quantify the risk of cable fires [16]; Ding et al applied a fault tree model to influence the urban underground integrated pipeline corridor project PPP model risk and concluded that the project risk has a greater impact on the factors and found that the application of the PPP model in underground integrated pipeline corridors is more suitable for developed regions [17]; Zhou et al and Seong et al, on the other hand, analyzed the key risks of urban integrated pipeline corridors and their ratings from the actual situation of Chinese as well as Korean cities, respectively. As early as 2011, Sousa et al proposed a method to systematically access and manage tunnel-related risks by combining a geological prediction model with a construction strategy decision model to predict the geology prior to tunnel construction to select the least risky construction strategy among different construction strategies [18]; Golam et al proposed a Bayesian belief network for assessing the risk of failure of metal water pipes model that can rank water supply trunk pipes in distribution networks to identify vulnerable and sensitive pipes for rational water supply management [19]; Zhang et al proposed a method for tunnel fire safety risk analysis based on fuzzy Bayesian networks [20]; Khwaja et al proposed a new public-private partnership based on fuzzy integral infrastructure project (PPP project) risk assessment method to help stakeholders make risk management decisions [21]; Wu et al developed a cloud model-based risk assessment model for shield construction in underground tunnels, which effectively addressed the stochastic uncertainty and fuzzy uncertainty of indicator factors [22]; in addition to these, the literature [23][24][25] also implemented dynamic analysis for underground tunnels and subsea tunnels; and the dynamic analysis of cable risks was realized.…”

The Main Impacts of Infrastructure Works on Public Roads

“…Urban infrastructure works have aroused the interest of researchers due to the increased demand for infrastructure services and its potential to produce social, economic, and environmental impacts. The increased demand for infrastructure services is directly related to factors such as the accelerated growth of cities [1][2][3][4][5] and the effects of climate change, such as flash floods [6].…”