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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F20%3A00007633" target="_blank" >RIV/46747885:24210/20:00007633 - isvavai.cz</a>
Alternative codes found
RIV/46747885:24620/20:00007633
Result on the web
<a href="https://www.mdpi.com/2076-3417/10/9/3132" target="_blank" >https://www.mdpi.com/2076-3417/10/9/3132</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app10093132" target="_blank" >10.3390/app10093132</a>

Result language
angličtina
Original language name
A Comparative Study of the Data-Driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Study
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
21100 - Other engineering and technologies

Project
<a href="/en/project/EF16_025%2F0007293" target="_blank" >EF16_025/0007293: Modular platform for autonomous chassis of specialized electric vehicles for freight and equipment transportation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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