Vulnerability Characteristic Analysis of Urban Group Buildings Based on the Learning and Reasoning of Bayesian Network

Abstract

This paper takes the building in Huizhou district of Guangdong Province as the research object, selecting the building with typical destructive features as samples. First of all, through the comparative analysis of seismic hazard factors of the sample building such as building structure types, building floors and years of construction after structure stress research, Bayesian network of structural vulnerability characteristics analysis of buildings based on Python is constructed and then the fitting prediction is performed. The method solves errors caused by the correlation of the index in the traditional prediction process, combines the prior information and the distribution characteristics of the training samples effectively. Finally, the method is verified via typical characteristic data of Huizhou and it compares with traditional Bayesian earthquake prediction methods, proving the advantages and reliability of the method. Use GIS to image related data.