Order reprints

Modal identification of a high-rise building subjected to a landfall typhoon via both deterministic and Bayesian methods

Yuncheng He Zhen Liu Zhi Li Jiurong Wu Jiyang Fu

*Corresponding author: Jiyang Fu jiyangfu@gzhu.edu.cn

MBE2019,6,7155doi:10.3934/mbe.2019359

Modal identification involves primarily the determination of natural frequencies, damping ratios, mode shapes of a dynamic system, etc. It is usually regarded as an essential task in a wide branch of structural dynamics and civil engineering, such as structural vibration control and damage identification of buildings or bridges. There are many modal identification techniques. Basically, these techniques can be categorized into two groups: deterministic methods and Bayesian approaches. The first group can be used to provide deterministic (or optimal) estimations of modal parameters, but they are unable to quantify the estimation uncertainties. The second group is based on a usage of the Bayesian framework. Compared to the first group, the second group of methods has a typical merit of being able to offer uncertainty information of identified parameters, which is of great interests, or even necessary, for some follow-up studies. In this paper, both a deterministic method, i.e., a combination of spectral analysis, filtering and Random Decrement Technique (RDT), and a Bayesian method, i.e., Bayesian Spectral Density Approach (BSDA), are exploited to experimentally identify the modal parameters of a 303 m high-rise building that was subjected to a landfall typhoon. The validity and efficiency of each method is verified by comparing the two kinds of results. Meanwhile, the identified modal parameters are used for the serviceability assessment of this high-rise building against some frequency-specific criteria.

Please supply your name and a valid email address you yourself

Fields marked*are required

Article URL   http://www.aimspress.com/MBE/article/4041.html
Article ID   mbe-16-06-359
Editorial Email  
Your Name *
Your Email *
Quantity *

Copyright © AIMS Press All Rights Reserved