Damage Correlation Analysis of Truss Structures Based on Monte Carlo Method and Bayesian Updating

In the field of structural engineering, the degradation of structural performance is an inherent characteristic that is difficult to avoid, making structural damage identification a core research topic for ensuring engineering safety. This study addresses the damage identification problem in truss structures by integrating the Monte Carlo method with Bayesian updating theory. A finite element numerical model of the truss structure was established using OpenSees to conduct research on damage parameter updating, systematically revealing the correlation between monitoring information and the stiffness parameters of members requiring updating. The results demonstrate that highly correlated monitoring information exerts a significant positive effect on parameter updating. Under the premise of ensuring parameter identification accuracy, the quantity of detection information can be reduced to effectively lower monitoring costs. The information quality evaluation framework developed in this study provides theoretical support for data optimization and screening in structural damage identification, offering valuable references for the construction of practical structural health monitoring systems. This research contributes methodological insights for enhancing the efficiency and economy of damage detection in engineering structures.