Parametric estimation of dispersive viscoelastic layered media with application to structural health monitoring. Issue 105 (February 2018)

• Record Type: Journal Article
• Authors: Ebrahimian, Hamed; Kohler, Monica; Massari, Anthony; Asimaki, Domniki
• Is Part Of: Soil dynamics and earthquake engineering. Issue 105(2018)
• Page Start: 204
• Page End: 223
• Publication Date: 2018-02
• Subjects: Wave inversion -- Bayesian estimation -- Dispersion -- Interferometry -- Frequency domain identification -- Structural system identification; Soil dynamics -- Periodicals; Earthquake engineering -- Periodicals; Sols -- Dynamique -- Périodiques; Génie parasismique -- Périodiques; 624.176205
• DOI: 10.1016/j.soildyn.2017.10.017
• Languages: English
• ISSNs: 0267-7261

Abstract

Abstract: We present a sequential Bayesian estimation method to estimate the material parameters that govern the one-dimensional propagation of shear waves through continuous, layered, viscoelastic solids. While the proposed estimation method is generic, namely, can be applied to waveform inversion problems that satisfy the above conditions, we here employ it for system identification of building structures. We approximate the linear-elastic response of building structures subjected to low-amplitude earthquake base excitations by a multilayer dispersive shear beam model with Kelvin-Voigt material subjected to vertically propagating shear waves. Utilizing the proposed sequential Bayesian estimation method, we sequentially update the probability distribution function of the unknown parameters to reduce the discrepancies between the estimated and measured frequency response functions. We next verify and validate the performance of the proposed estimation method and investigate the limitations of the presented structural system identification approach using two case studies. In the first case study, we use the simulated structural response of a three-dimensional 52-story building model subjected to bi-directional low-amplitude ground shakings. We estimate the frequency-dependent phase velocity and damping ratio, as well as the mass distribution along the building height. Then, we verify the structural damage detection and localization capabilities of the presented system identification approach by comparing the wave model parameters estimated from simulated response of undamaged and damaged structural models. In the second case study, we use data measured from a shake table experiment on a full-scale five-story reinforced concrete building specimen, where the estimated wave model parameters capture the progressive structural damage in the test specimen. The validation studies suggest that the sequential Bayesian estimation method based on viscoelastic dispersive wave propagation can be used for system and damage identification of building structures. Highlights: We presented a Bayesian estimation method for frequency-domain seismic interferometry. This method provides a generic algorithm for wave inversion and uncertainty quantification. We applied this method for structural system and damage identification. For this purpose, a building is identified as a multilayer dispersive Kelvin-Voigt model. We performed two verification and validation case studies.