Subway Tunnel Disease Associations Mining Based on Fault Tree Analysis Algorithm

Abstract

Monitoring and control of subway tunnel diseases throughout operation determine whether the operation of the subway is safe or not. In order to ensure operation safety, in-depth analysis of tunnel disease risks must be conducted. We constructed a fault tree based on tunnel diseases of Shanghai Subway at first. Using the subway tunnel maintenance work data, we calculated the probability of occurrence of elementary events of the fault tree, conducted quantitative calculation and analysis on the tunnel diseases, and found major diseases of the tunnels and their causes in light of the calculation results. Then, indicated by the precise fault tree analysis (FTA) we conducted, common tunnel diseases mainly include large passenger flow, shortage of maintenance personnel, maintenance error, personal carelessness, hot weather, and poor lighting. Analysis was conducted on the probability importance of elementary events of the tunnel diseases as well. In the end, we proposed the tunnel disease association rule mining algorithm based on the support degree. Via the calculation of association among major diseases, we explored the elaborate association mechanism of the diseases. The in-depth mining on the association mechanism can provide theoretical support and decision support for prevention and comprehensive control of the tunnel diseases and lay a solid foundation of practice guidance for subway operation safety of megacities.