Particle Filtering Based Structural Assessment with Acoustic Emission Sensing

dc.contributor.author Yan, Wuzhao
dc.contributor.author Abdelrahman, Marwa
dc.contributor.author Zhang, Bin
dc.contributor.author Ziehl, Paul
dc.date 2018-02-17T23:47:51.000
dc.date.accessioned 2020-06-30T06:54:46Z
dc.date.available 2020-06-30T06:54:46Z
dc.date.issued 2016-01-01
dc.description.abstract <p>Nuclear structures are designed to withstand severe loading events under various stresses. Over time, aging of structural systems constructed with concrete and steel will occur due to corrosion of reinforcement [1], alkali-silica reaction [2], and other mechanisms. This deterioration of structural integrity, if not detected in time, may reduce service life of nuclear facilities and/or lead to unnecessary or untimely repairs. For this reason, online monitoring of structures in nuclear power plants and waste storage has drawn significant attention in recent years [3]. Of many existing non-destructive evaluation (NDE) approaches, acoustic emission is promising for assessment of structural damage because it is non-intrusive and is sensitive to corrosion and crack growth in reinforced concrete elements.</p> <p>To provide a rapid, actionable, and graphical means for interpretation of acoustic emission data, Intensity Analysis plots have been developed [1]. This approach provides a means for classification of damage. Since the acoustic emission measurement is only an indirect indicator of structural damage, potentially corrupted by non-genuine data, it is suitable to estimate the states of corrosion and cracking in a Bayesian estimation framework. In this paper, we will utilize the structural accelerated corrosion data from a specimen at the University of South Carolina to develop a particle filtering-based diagnosis and prognosis algorithm [4]. The promising features of the proposed algorithm will be demonstrated from two aspects: one is that it is able to provide a more accurate estimation of corrosion state; and the other is that it is able to predict the service time when the structural strength, defined by cross-sectional area reduction, degrades to a predefined threshold. The paper will formulate the structural health monitoring problem with a particle filtering algorithm, investigate the corrosion degradation modeling, design the diagnostic and prognostic algorithms, and define performance metrics for verification and validation. The results will also be compared with the Intensity Analysis plot approach.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/qnde/2016/abstracts/73/
dc.identifier.articleid 5080
dc.identifier.contextkey 9290654
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath qnde/2016/abstracts/73
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/62165
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/qnde/2016/abstracts/73/291_Particle_Filtering.pdf|||Sat Jan 15 01:45:55 UTC 2022
dc.subject.disciplines Acoustics, Dynamics, and Controls
dc.subject.disciplines Materials Science and Engineering
dc.subject.disciplines Structural Engineering
dc.title Particle Filtering Based Structural Assessment with Acoustic Emission Sensing
dc.type event
dc.type.genre event
dspace.entity.type Publication
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