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008			210201s2021 gw a o |||| 0|eng d
020			_a9783030662592
024	7		_a10.1007/978-3-030-66259-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aTA645 _b2021 EB
100	1		_aEntezami, Alireza _eautor _0(orcid)0000-0002-4864-2120 _1https://orcid.org/0000-0002-4864-2120 _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aStructural Health Monitoring by Time Series Analysis and Statistical Distance Measures _cby Alireza Entezami.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XII, 136 páginas) _b3 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aPoliMI SpringerBriefs _x2282-2577
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
520	3		_aThis book conducts effective research on data-driven Structural Health Monitoring (SHM), and accordingly presents many novel feature extraction methods by time series analysis and signal processing, to extract reliable damage sensitive features from vibration responses. In this regard, some limitations of time series modeling are dealt with. For decision-making, innovative distance-based novelty detection techniques are presented to detect, locate, and quantify different damage scenarios. The performance of the presented methods is demonstrated via laboratory and full-scale structures along with several comparative studies. The main target audience of the book includes scholars, graduate students working on SHM via statistical pattern recognition in terms of feature extraction and classification for damage diagnosis under environmental and operational variations; it would also be beneficial for practicing engineers whose work involves these topics.
988			_aSpringer_Engineering_2021
650		7	_2embne _aAnálisis estructural (Ingeniería) _9150495
650		7	_2embne _aEdificios _xDiseño y construcción _9139419
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-66259-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2021 _dz _eb _zSI