000011220 001__ 11220
000011220 005__ 20141205155848.0
000011220 04107 $$aeng
000011220 046__ $$k2008-10-12
000011220 100__ $$aOmenzetter, Piotr
000011220 24500 $$aApplication of Time Series Analysis and Statistical Pattern Recognition for Seismic Damage Detection

000011220 24630 $$n14.$$pProceedings of the 14th World Conference on Earthquake Engineering
000011220 260__ $$b
000011220 506__ $$arestricted
000011220 520__ $$2eng$$aTime series methods are inherently suited to the analysis of regularly sampled Structural Health Monitoring (SHM) data and deserve to be better and more extensively explored. This study focuses on the use of statistical pattern recognition techniques to classify seismic damage based on analysis of the time series model coefficients. Autoregressive (AR) models were used to analyze time histories from a 3-storey laboratory bookshelf structure excited on a shake table and the ASCE Phase II Experimental SHM Benchmark Structure in both healthy and damaged states. The coefficients of these AR models were used as damage sensitive features. Three supervised pattern recognition techniques, Back-propagation Artificial Neural Networks, Nearest Neighbor and Learning Vector Quantization were used to classify damage into states, quantify its severity and determine location. In order to visualize the data and reduce its dimensionality it was compressed using Principal Component Analysis or Sammon mapping. The minimum numbers of sensors required for reliable damage detection were also addressed. The results show that seismic damage can be detected and quantified by the three pattern recognition techniques with a very good accuracy using compressed data and small number of sensors.

000011220 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000011220 653__ $$aDamage Detection, Time Series Analysis, Pattern Recognition, Artificial Neural Networks, Nearest Neighbor, Learning Vector Quantization

000011220 7112_ $$a14th World Conference on Earthquake Engineering$$cBejing (CN)$$d2008-10-12 / 2008-10-17$$gWCEE15
000011220 720__ $$aOmenzetter, Piotr$$iDe Lautour, Oliver R
000011220 8560_ $$ffischerc@itam.cas.cz
000011220 8564_ $$s281321$$uhttps://invenio.itam.cas.cz/record/11220/files/11-0049.pdf$$yOriginal version of the author's contribution as presented on CD, Paper ID: 11-0049.
000011220 962__ $$r9324
000011220 980__ $$aPAPER