Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints

Hysteresis is a nonlinear phenomenon present in many structures, such as those assembled by bolted joints. Despite large number of recent findings related to identification techniques for these systems, the problem still challenging and open contributions. To fill gap concerning proposition algorithms based on closed-form solutions, this work introduces use harmonic balance method identify stochastic Bouc–Wen model predicting behavior structures. A piecewise smooth procedure applied hysteretic restoring force become possible derive an analytical approximation response Fourier series. Firstly, used calibrate deterministic parameters minimizing error between amplitudes numerical extracted from experimental data using cross-entropy optimization method. Since investigated here contain variability due measurement process (aleatoric uncertainties), are then priori conditions update their probability density functions via Bayesian inference. Having random variables, obtained. This methodology was illustrated structure benchmark. The results indicate that proposed can accurate dynamics even taking into account variability.