Fire Risk Assessment in Universities Based on Fault Tree Analysis and Bayesian Network

Abstract

In order to scientifically and accurately analyze the fire hazards existing in colleges and universities, and put forward feasible suggestions for eliminating hidden dangers, a fire risk assessment method based on Fault tree analysis and Bayesian network is proposed.Firstly, the Fault tree of the large loss caused by the fire in the university premises is constructed, and then the Fault tree is transformed into a Bayesian network model. In this method, the failure risk of the fire protection system can be obtained by forward reasoning according to the probability of the basic events in the fire, and finally, the reliability of the fire protection system of the whole university is analyzed.At the same time, combined with the reverse diagnosis and reasoning technology of Bayesian network, according to the known or assumed state of leaf nodes, the posterior probability and probability importance degree of each root node can be reversed to check the weak links in the fire protection system. In the conclusion, suggestions and rectification strategies are put forward for the fire protection system in colleges and universities.This paper proposes a method that combines fault tree analysis with Bayesian network and applies it to fire risk assessment in colleges and universities. Based on the fire protection big data, the software Genie3.0 is used to construct a Bayesian network model of fire risk in universities, and an example analysis of fire risk assessment is carried out by taking a university in Nanjing as an example, which not only analyzes the reliability of the entire system, but also It also uses the bidirectional reasoning ability of Bayesian network to analyze the weak link performance of the system, which improves the model's description ability and inference computing ability. It is proved that the FTA-BN method has application potential in the field of fire risk assessment.

Keywords: fire risk assessment, Fault tree analysis, Bayesian network, probability importance degree

DOI: 10.54941/ahfe1002846