Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge

Journal of Sensor and Actuator Networks

Panagiotis Panetsos¹  Costas Argyris²  Costas Papadimitriou²  Panagiotis Tsopelas³ 

[1] Capital Maintenance Department, Egnatia Odos S.A., 57001 Thermi, Greece;Department of Mechanical Engineering, University of Thessaly, 38334 Volos, Greece;Department of Mechanics, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, 15773 Athens, Greece;

关键词: Bayesian inference;    model updating;    modal identification;    structural dynamics;    bridges;   

DOI  :  10.3390/jsan9020027

来源: DOAJ

【 摘 要 】

A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The framework is demonstrated by performing model-updating for the Metsovo bridge using a reduced high-fidelity finite element model. Experimental modal identification methods are used in order to extract the modal characteristics of the bridge from ambient acceleration time histories obtained from field measurements exploiting a network of reference and roving sensors. The Transitional Markov Chain Monte Carlo algorithm is used to perform the model updating by drawing samples from the posterior distribution of the model parameters. The proposed framework yields reasonable uncertainty bounds for the model parameters, insensitive to the redundant information contained in the measured data due to closely spaced sensors. In contrast, conventional Bayesian formulations which use probabilistic models to characterize the components of the discrepancy vector between the measured and model-predicted mode shapes result in unrealistically thin uncertainty bounds for the model parameters for a large number of sensors.

【 授权许可】

Unknown