000161859 001__ 161859
000161859 005__ 20190316235010.0
000161859 0247_ $$2doi$$a10.1016/j.compstruc.2010.01.001
000161859 022__ $$a0045-7949
000161859 02470 $$2ISI$$a000276156000007
000161859 037__ $$aARTICLE
000161859 245__ $$aMethodologies for model-free data interpretation of civil engineering structures
000161859 260__ $$bElsevier$$c2010
000161859 269__ $$a2010
000161859 336__ $$aJournal Articles
000161859 520__ $$aStructural health monitoring (SHM) has the potential to provide quantitative and reliable data on the real condition of structures, observe the evolution of their behaviour and detect degradation This paper presents two methodologies for model-free data interpretation to identify and localize anomalous behaviour in civil engineering structures Two statistical methods based on (i) moving principal component analysis and (ii) robust regression analysis are demonstrated to be useful for damage detection during continuous static monitoring of civil structures. The methodologies are tested on numerically simulated elements with sensors for a range of noise in measurements. A comparative study with other statistical analyses demonstrates superior performance of these methods for damage detection. Approaches for accommodating outliers and missing data, which are commonly encountered in structural health monitoring for civil structures, are also proposed. To ensure that the methodologies are scalable for complex structures with many sensors, a clustering algorithm groups sensors that have strong correlations between their measurements Methodologies are then validated on two full-scale structures: The results show the ability of the methodology to identify abrupt permanent changes in behavior. (C) 2010 Elsevier Ltd All rights reserved.
000161859 6531_ $$aStructural health monitoring
000161859 6531_ $$aData interpretation
000161859 6531_ $$aSignal processing
000161859 6531_ $$aPattern recognition
000161859 6531_ $$aPrincipal component analyses
000161859 6531_ $$aClustering
000161859 6531_ $$aDamage Detection
000161859 6531_ $$aMissing Data
000161859 6531_ $$aImputation Methods
000161859 6531_ $$aIdentification
000161859 6531_ $$aAlgorithm
000161859 6531_ $$aBehavior
000161859 6531_ $$aValues
000161859 700__ $$aPosenato, D.
000161859 700__ $$0240073$$g171659$$aKripakaran, P.
000161859 700__ $$aInaudi, D.
000161859 700__ $$aSmith, I.F.C.$$g106443$$0241981
000161859 773__ $$j88$$tComputers and Structures$$k7/8$$q467-482
000161859 8564_ $$uhttps://infoscience.epfl.ch/record/161859/files/Posenato-et-al-Postprint-Noise-out-2010-CS.pdf$$zn/a$$s1265389$$yn/a
000161859 909C0 $$xU10237$$0252031$$pIMAC
000161859 909CO $$ooai:infoscience.tind.io:161859$$qGLOBAL_SET$$particle$$pENAC
000161859 917Z8 $$x191461
000161859 917Z8 $$x106443
000161859 937__ $$aEPFL-ARTICLE-161859
000161859 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000161859 980__ $$aARTICLE