000 03148nam a22005055i 4500
001 978-3-319-02559-9
003 DE-He213
005 20140220082511.0
007 cr nn 008mamaa
008 140121s2014 gw | s |||| 0|eng d
020 _a9783319025599
_9978-3-319-02559-9
024 7 _a10.1007/978-3-319-02559-9
_2doi
050 4 _aTA355
050 4 _aTA352-356
072 7 _aTGMD4
_2bicssc
072 7 _aTEC009070
_2bisacsh
072 7 _aSCI018000
_2bisacsh
082 0 4 _a620
_223
100 1 _aEftekhar Azam, Saeed.
_eauthor.
245 1 0 _aOnline Damage Detection in Structural Systems
_h[electronic resource] :
_bApplications of Proper Orthogonal Decomposition, and Kalman and Particle Filters /
_cby Saeed Eftekhar Azam.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXII, 135 p. 87 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-530X
505 0 _aIntroduction -- Recursive Bayesian estimation of partially observed dynamic systems -- Model Order Reduction of dynamic systems via Proper Orthogonal Decomposition -- POD-Kalman observer for linear time invariant dynamic systems -- Dual estimation and reduced order modeling of damaging structures -- Summary of the recursive Bayesian inference schemes.
520 _aThis monograph assesses in depth the application of recursive Bayesian filters in structural health monitoring. Although the methods and algorithms used here are well established in the field of automatic control, their application in the realm of civil engineering has to date been limited. The monograph is therefore intended as a reference for structural and civil engineers who wish to conduct research in this field. To this end, the main notions underlying the families of Kalman and particle filters are scrutinized through explanations within the text and numerous numerical examples. The main limitations to their application in monitoring of high-rise buildings are discussed, and a remedy based on a synergy of reduced order modeling (based on proper orthogonal decomposition) and Bayesian estimation is proposed. The performance and effectiveness of the proposed algorithm is demonstrated via pseudo-experimental evaluations.
650 0 _aEngineering.
650 0 _aVibration.
650 0 _aBuilding construction.
650 1 4 _aEngineering.
650 2 4 _aVibration, Dynamical Systems, Control.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aBuilding Repair and Maintenance.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319025582
830 0 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-530X
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-02559-9
912 _aZDB-2-ENG
999 _c92880
_d92880