Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24279
Title: Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks
Authors: Ni, YQ 
Zhou, XT
Ko, JM
Issue Date: 2006
Publisher: Academic Press
Source: Journal of sound and vibration, 2006, v. 290, no. 1-2, p. 242-263 How to cite?
Journal: Journal of sound and vibration 
Abstract: This paper presents an experimental investigation of seismic damage identification of a 38-storey tall building model using measured frequency response functions (FRFs) and neural networks (NNs). The 1:20 scale reinforced concrete structure is tested on a shaking table by exerting successively enhanced ground earthquake excitation to generate trifling, moderate, serious and complete (nearly collapsed) damage, respectively. After incurring the earthquake excitations at each level, a 20-min white-noise random excitation of low intensity is applied to the structure to produce ambient vibration response, from which FRFs are measured for post-earthquake damage detection by means of the NN technology. Principal component analysis (PCA) is pursued to the measured FRFs for dimensionality reduction and noise elimination, and then the PCA-compressed FRF data are used as input to NNs for damage identification. After a study on tolerance of PCA-reconstructed FRFs to measurement noise, different PCA configurations are designed for overall damage evaluation and damage location (distribution) identification, respectively. It is shown that the identification results by means of the FRF projections on a few principal components are much better than those directly using the measured FRF data, and agree fairly well with the visual inspection results of seismic damage during tests.
URI: http://hdl.handle.net/10397/24279
ISSN: 0022-460X
EISSN: 1095-8568
DOI: 10.1016/j.jsv.2005.03.016
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

76
Last Week
0
Last month
1
Citations as of Aug 14, 2017

WEB OF SCIENCETM
Citations

60
Last Week
1
Last month
1
Citations as of Aug 21, 2017

Page view(s)

38
Last Week
4
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.