Abstract Summary
The evaluation of the actual state of structural systems based on available response measurements is a relevant aspect in civil engineering. To this end, Bayesian model updating provides a sound theoretical framework that allows to include the unavoidable uncertainties arising in modeling and monitoring processes. The effective treatment of high-dimensional parameter spaces in this framework represents one of the open challenges for practical applications, especially for complex non-linear structural models. To address this issue, an effective implementation of subset simulation is considered [1], which relies on the formulation of an equivalent reliability problem [2]. In this setting, failure samples follow the posterior distribution of the original identification problem. Overall, the proposed implementation does not rely on any problem-specific information, avoids the need for knowledge on the maximum likelihood value, and requires minimal modifications to the standard subset simulation algorithm. For an efficient numerical implementation, a parametric reduced-order model formulation based on substructure coupling for dynamic analysis is considered [3]. An application example involving a three-dimensional bridge model equipped with nonlinear devices is presented to illustrate the capabilities of the proposed approach. References: [1] D. J. Jerez, H. A. Jensen, and M. Beer, “An effective implementation of reliability methods for Bayesian model updating of structural dynamic models with multiple uncertain parameters,” Reliability Engineering & System Safety 225:108634, 2022. [2] D. Straub and I. Papaioannou, “Bayesian updating with structural reliability methods,” Journal of Engineering Mechanics 141(3):04014134, 2015. [3] H. A. Jensen and C. Papadimitriou, “Sub-structure coupling for dynamic analysis: Application to complex simulation-based problems involving uncertainty,” Springer-Verlag GmbH, 2019.
