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.authorShokravi, Hoofar
dc.contributor.authorShokravi, Hooman
dc.contributor.authorBakhary, Norhisham
dc.contributor.authorKoloor, Seyed Saeid Rahimian
dc.contributor.authorPetrů, Michal
dc.date.accessioned2020-04-30T10:37:43Z
dc.date.available2020-04-30T10:37:43Z
dc.date.issued2020-03-29
dc.description.abstractSubspace 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.extent18 strancs
dc.identifier.WebofScienceResearcherIDG-6623-2013 Petrů, Michal
dc.identifier.doi10.3390/app10093132
dc.identifier.orcid0000-0002-7643-8450 Petrů, Michal
dc.identifier.orcid0000-0002-1820-6379 Koloor, Seyed Saeid Rahimian
dc.identifier.urihttps://dspace.tul.cz/handle/15240/154783
dc.identifier.urihttps://www.mdpi.com/2076-3417/10/9/3132
dc.language.isocscs
dc.publisherMDPI
dc.relation.ispartofApplied Sciences 2020
dc.subjectstructural health monitoring (SHM)cs
dc.subjectsubspace system identification (SSI)cs
dc.subjectprinciple components (PC)cs
dc.titleA Comparative Study of the Data-Driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Studycs
local.identifier.publikace7633
local.relation.issue9
local.relation.volume10
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