Structural Health Monitoring Based on Bayesian Updating Theory

Truss structures are widely implemented worldwide, and their structural safety issues have garnered significant societal attention. Due to their extended service life, material aging in truss structures becomes inevitable, potentially creating safety hazards during routine usage. Under these circumstances, health monitoring of truss structures becomes crucial. This paper employs Bayesian updating theory combined with rejection sampling methodology, utilizing a simple truss structure as an experimental case study for structural health monitoring, to explore the potential applications of Bayesian updating theory in this field. The results demonstrate that data obtained from proper loading modes can lead to favorable updating effects. Different load modes have different updating effects on web members and chord members. Preliminary outcome prediction prior to updating can help avoid resource wastage. Multiple measuring points should be used to improve the update accuracy when collecting the load information of the members. The findings of this study can provide valuable references for practical engineering applications.