Global and Local Damage Assessment of Structures by Seismic Response Data
Date Issued
2007
Date
2007
Author(s)
Lin, Yu-Chia
DOI
en-US
Abstract
This study presents two different damage detection algorithms: one is for global damage detection and the other is for local damage detection. For damage detection three steps are proposed: (1) the state-space model identification from input-output measurements to extract the physical parameters, (2) global damage detection using damage locating vector (DLV) method, (3) using the changes of curvature of mode shapes as damage index to localize the position of structural damage for more detail local damage detection.
For state space identification, SRIM method is used to identify the system matrix from input-output measurements and the second-order structural system matrix are also identified from realization model. For damage detection, the pre- and post- damaged structures are identified using the two separate equivalent linear models. In this study four numerical models are selected: (1) a three degree of freedom shear-type Structure, (2) a two-dimensional truss structure, (3) a two span continuous beam represented by finite element model and (4) a three-story finite element model, as examples for global and local damage detection in this study.
Besides, these damage detection methods are also applied to experimental data. Shaking table test of two different benchmark models for SHM research in NCREE is selected for the experimental study. One of the benchmark models is the 3-story steel frame with adding brace at its first floor to simulate the healthy state and removing bracing to simulate the damaged state; the other is the wedge-type cut on the flange of the column at first floor to simulate the damaged state. Because of the limit number of sensors in the implementation, the displacement and strain measurements are also used to extract the mode shape of damaged floor for local damage detection through the usage of damage index.
Subjects
系統識別
傷害檢測
System Identification
Damage Detection
Type
thesis
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