A new gibbs-sampling based algorithm for Bayesian Model updating of linear dynamic systems with incomplete complex modal data

• المؤلفون الرئيسيون: Hung, Cheung Sai, Bansal, Sahil
• مؤلفون آخرون: School of Civil and Environmental Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2013
• الموضوعات: DRNTU::Engineering::Civil engineering
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/105851; http://hdl.handle.net/10220/17969; http://www.iaeng.org/publication/IMECS2013/

Model updating using measured system dynamic response has a wide range of applications in structural health monitoring, control and response prediction. In this paper, we are interested in model updating of a linear structural dynamic system with non-classical damping based on incomplete modal data including modal frequencies, damping ratios, and partial complex mode shapes of some of the dominant modes. To quantify the uncertainties and plausibility of the model parameters, a Bayesian approach is adopted. A new Gibbssampling based algorithm is proposed that allows for an efficient update of the probability distribution of the model parameters. The effectiveness and efficiency of the proposed method are illustrated by a numerical example involving a linear structural dynamic system with complex modes.