Vulnerability analysis of super high-rise building security system based on Bayesian network and digital twin technology

Recent years have seen the increasing need to prevent illegal intrusions in super high-rise buildings. To improve the security system of super high-rise buildings by adding dynamic vulnerability assessments for illegal intrusion risks and rectification plan optimization capabilities, while also to increase its visualization level, a method utilizing digital twin technology and integrating Bayesian networks is proposed. To validate this method, a case study within a prominent super high-rise building in the Central Business District (CBD) of Beijing is conducted, in which the vulnerability analysis results have shown that among all the tertiary parent nodes, the coverage extent of the video surveillance system most significantly influences the vulnerability. By using the comprehensive blind spot analysis function of the established digital twin platform, thoughtful additions and angle adjustments of surveillance cameras have successfully increased the score of the security level from 84 to 90. This has proved that the method proposed in this study can provide decision support for the vulnerability assessment and improvement for the security system of super high-rise buildings.