000013633 001__ 13633
000013633 005__ 20161114170125.0
000013633 04107 $$aeng
000013633 046__ $$k2011-05-25
000013633 100__ $$aSaad, G.
000013633 24500 $$aRobust Structural Health Monitoring Using a Polynomial Chaos Based Sequential Data Assimilation Technique

000013633 24630 $$n3.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000013633 260__ $$bNational Technical University of Athens, 2011
000013633 506__ $$arestricted
000013633 520__ $$2eng$$aWith the recent technological advances and the evolution of advanced smart systems for damage detection and signal processing, Structural Health Monitoring (SHM) emerged as a multidisciplinary field with wide applicability throughout the various branches of engineering, mathematics and physical sciences. However, significant challenges associated with modeling the physical complexity of systems comprising these structures remain. This is mainly due to the fact that numerous uncertainties associated with modeling, parametric and measurement errors could be introduced. In cases where these uncertainties are significant, standard identification and damage detection techniques are either unsuitable or inefficient. This study presents a robust data assimilation approach based on a stochastic variation of the Kalman Filter where polynomial functions of random variables are used to represent the inherent process uncertainties. The presented methodology is combined with a non-parametric modeling technique to tackle structural health monitoring of a four-story shear building. The structure is subject to a base motion specified by a time series consistent with the ElCentro earthquake and undergoes a preset damage in the first floor. The purpose of the problem is localizing the damage in both space and time, and tracking the state of the system throughout and subsequent to the damage time. The application of the introduced data assimilation technique to SHM enhances the latter’s applicability to a wider range of structural problems with strongly nonlinear dynamical behavior and with uncertain and complex governing laws.

000013633 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000013633 653__ $$aStructural Health Monitoring, Sequential Data Assimilation, Kalman Filter, Uncertainty Quantification, Polynomial Chaos.

000013633 7112_ $$aCOMPDYN 2011 - 3rd International Thematic Conference$$cIsland of Corfu (GR)$$d2011-05-25 / 2011-05-28$$gCOMPDYN2011
000013633 720__ $$aSaad, G.$$iGhanem, R.
000013633 8560_ $$ffischerc@itam.cas.cz
000013633 8564_ $$s3567186$$uhttp://invenio.itam.cas.cz/record/13633/files/410.pdf$$yOriginal version of the author's contribution as presented on CD, section: MS 12 Identification Methods in Structural Dynamics.
000013633 962__ $$r13401
000013633 980__ $$aPAPER