A Comparative Study of the Data-Driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Study

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dc.contributor.author Shokravi, Hoofar
dc.contributor.author Shokravi, Hooman
dc.contributor.author Bakhary, Norhisham
dc.contributor.author Koloor, Seyed Saeid Rahimian
dc.contributor.author Petrů, Michal
dc.date.accessioned 2020-04-30T10:37:43Z
dc.date.available 2020-04-30T10:37:43Z
dc.date.issued 2020-03-29
dc.identifier.uri https://dspace.tul.cz/handle/15240/154783
dc.identifier.uri https://www.mdpi.com/2076-3417/10/9/3132
dc.description.abstract Subspace system identification is a class of methods to estimate state-space model based on low rank characteristic of a system. State-space-based subspace system identification is the dominant subspace method for system identification in health monitoring of the civil structures. The weight matrices of canonical variate analysis (CVA), principle component (PC), and unweighted principle component (UPC), are used in stochastic subspace identification (SSI) to reduce the complexity and optimize the prediction in identification process. However, researches on evaluation and comparison of weight matrices’ performance are very limited. This study provides a detailed analysis on the effect of different weight matrices on robustness, accuracy, and computation efficiency. Two case studies including a lumped mass system and the response dataset of the Alamosa Canyon Bridge are used in this study. The results demonstrated that UPC algorithm had better performance compared to two other algorithms. It can be concluded that though dimensionality reduction in PC and CVA lingered the computation time, it has yielded an improved modal identification in PC. cs
dc.format.extent 18 stran cs
dc.language.iso cs cs
dc.publisher MDPI
dc.relation.ispartof Applied Sciences 2020
dc.subject structural health monitoring (SHM) cs
dc.subject subspace system identification (SSI) cs
dc.subject principle components (PC) cs
dc.title A Comparative Study of the Data-Driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Study cs
dc.identifier.doi 10.3390/app10093132
local.relation.volume 10
local.relation.issue 9
local.identifier.publikace 7633
dc.identifier.orcid 0000-0002-7643-8450 Petrů, Michal
dc.identifier.orcid 0000-0002-1820-6379 Koloor, Seyed Saeid Rahimian
dc.identifier.WebofScienceResearcherID G-6623-2013 Petrů, Michal

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