Bayesian model identification of higher-order frequency response functions for structures assembled by bolted joints

• Autor(a) principal: Teloli, Rafael de O. [UNESP]
• Data de Publicação: 2021
• Outros Autores: da Silva, Samuel [UNESP], Ritto, Thiago G., Chevallier, Gaël
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.ymssp.2020.107333; http://hdl.handle.net/11449/206750
• topic: Bayesian identification; Higher-order frequency response function; Hysteretic systems; Jointed structures; Volterra series

This paper proposes a procedure to identify a stochastic Bouc-Wen model for describing the dynamics of a structure assembled by bolted joints considering vibration data. The proposed identification approach is expressed into a Bayesian framework to take into account the data fluctuations related to uncertainties in the measurement process. The calibration of the model parameters uses the analytical expressions of the higher-order frequency response functions (FRFs) for approximating experimental measurements. The Metropolis-Hastings algorithm is employed for approximating posterior distributions. Once calibrated, the applicability of the probabilistic Bouc-Wen model is evaluated, and its dynamical behavior is compared with experimental measurements from the bolted structure. The results show that the stochastic version of the Bouc-Wen model can predict with adequate agreement, including hysteretic effects, the output of the jointed structure considering several excitation amplitudes.