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|Title:||Vibration-based damage detection of tall building structures||Authors:||Zhou, Xiantong||Keywords:||Hong Kong Polytechnic University -- Dissertations
Tall buildings -- Earthquake effects -- Testing
|Issue Date:||2004||Publisher:||The Hong Kong Polytechnic University||Abstract:||This dissertation is concerned with vibration-based seismic damage detection of tall building structures, based on the measurement dynamic signals directly, rather than on structure modeling and numerical simulation. For the purpose of studying earthquake response behaviours and vibration-based seismic damage detection, a scaled tall building model structure simulating one typical high-rise residential building in Hong Kong is elaborately fabricated and tested on shaking table. Several earthquake records simulating different soil site condition are designed and exerted on the model structure. The excitation magnitude of such earthquakes is enhanced successively. Following each level of earthquakes, visual inspection on incurred seismic damage is conducted and white-noise exciting tests are performed, providing the basis for the subsequent damage detection. Generally speaking, vibration-based damage identification usually needs to develop a mechanics model of the investigated structure, for deriving the required dynamic characteristics. However, it is a laborious work to develop a mechanics model for highly complex building structure, which also cannot fulfill immediate post-earthquake damage assessment requirement. Further, apart from modal identification error, structure model introduces additional modeling error, which increases uncertainty greatly in model-based damage identification. Therefore, a variety of model-free damage identification or evaluation approaches, based on the measured signals directly, are developed in this study.
Firstly, an approach of damage identification is developed, employing both excitation and response data under white-noise exerting. Considering the availability of excitation information recorded during shaking table tests and possible modal identification errors, the identification is based on the computed frequency response functions (FRFs) directly, rather than on ultimate modal parameters derived from FRFs. Taking obtained FRFs as input, neural networks are constructed for damage location and severity identification. However, high dimensionality of FRF hurdles the neural network training convergence. Using principal component analysis (PCA) technique, feature extraction on the FRFs is executed, where the functionality of PCA for information compression as well as measurement noises filtering is investigated in detail. This part of study mainly explores damage identification potential of using non-frequency or non-mode shape dynamic characteristics, when both excitation and response data are available. From practical point of view, only response signals can be measured in real building structure, whereas excitation information is usually unavailable. As a result, FRFs can not be obtained. Approaches of output-only modal identification utilizing only white-noise excited response for the tall building model structure are developed, based on which such modal parameters as frequency and mode shape are calculated. Thereafter, using these estimated dynamic parameters, damage indicators or damage indices are constructed and compared for their performances on seismic damage location and intensity recognition. For damage identification of building structures under earthquake attacking, it is more important to evaluate incurred damage, shortly after earthquake occurs. Accordingly, earthquake records themselves should be utilized directly for post-earthquake assessment, avoiding the necessary of extra modal testing. Eventually, the emphasis of this study is placed on exploring the potential of earthquake records - based damage evaluation. The evaluation is at overall and almost real-time senses.
|Description:||1 v. (various pagings) : ill. ; 30 cm.
PolyU Library Call No.: [THS] LG51 .H577P CSE 2004 ZhouX
|URI:||http://hdl.handle.net/10397/3816||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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Citations as of Jun 18, 2018
Citations as of Jun 18, 2018
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