Cargando…
Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method
The inverse problem of structural system identification is prone to ill-conditioning issues; thus, uniqueness and stability cannot be guaranteed. This issue tends to amplify the error propagation of both the epistemic and aleatory uncertainties, where aleatory uncertainty is related to the accuracy...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122277/ https://www.ncbi.nlm.nih.gov/pubmed/33919329 http://dx.doi.org/10.3390/s21092918 |
_version_ | 1783692572919595008 |
---|---|
author | Peng, Tian Nogal, Maria Casas, Joan R. Turmo, Jose |
author_facet | Peng, Tian Nogal, Maria Casas, Joan R. Turmo, Jose |
author_sort | Peng, Tian |
collection | PubMed |
description | The inverse problem of structural system identification is prone to ill-conditioning issues; thus, uniqueness and stability cannot be guaranteed. This issue tends to amplify the error propagation of both the epistemic and aleatory uncertainties, where aleatory uncertainty is related to the accuracy and the quality of sensors. The analysis of uncertainty quantification (UQ) is necessary to assess the effect of uncertainties on the estimated parameters. A literature review is conducted in this paper to check the state of existing approaches for efficient UQ in the parameter identification field. It is identified that the proposed dynamic constrained observability method (COM) can make up for some of the shortcomings of existing methods. After that, the COM is used to analyze a real bridge. The result is compared with the existing method, demonstrating its applicability and correct performance by a reinforced concrete beam. In addition, during the bridge system identification by COM, it is found that the best measurement set in terms of the range will depend on whether the epistemic uncertainty involved or not. It is concluded that, because the epistemic uncertainty will be removed as the knowledge of the structure increases, the optimum sensor placement should be achieved considering not only the accuracy of sensors, but also the unknown structural part. |
format | Online Article Text |
id | pubmed-8122277 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81222772021-05-16 Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method Peng, Tian Nogal, Maria Casas, Joan R. Turmo, Jose Sensors (Basel) Article The inverse problem of structural system identification is prone to ill-conditioning issues; thus, uniqueness and stability cannot be guaranteed. This issue tends to amplify the error propagation of both the epistemic and aleatory uncertainties, where aleatory uncertainty is related to the accuracy and the quality of sensors. The analysis of uncertainty quantification (UQ) is necessary to assess the effect of uncertainties on the estimated parameters. A literature review is conducted in this paper to check the state of existing approaches for efficient UQ in the parameter identification field. It is identified that the proposed dynamic constrained observability method (COM) can make up for some of the shortcomings of existing methods. After that, the COM is used to analyze a real bridge. The result is compared with the existing method, demonstrating its applicability and correct performance by a reinforced concrete beam. In addition, during the bridge system identification by COM, it is found that the best measurement set in terms of the range will depend on whether the epistemic uncertainty involved or not. It is concluded that, because the epistemic uncertainty will be removed as the knowledge of the structure increases, the optimum sensor placement should be achieved considering not only the accuracy of sensors, but also the unknown structural part. MDPI 2021-04-21 /pmc/articles/PMC8122277/ /pubmed/33919329 http://dx.doi.org/10.3390/s21092918 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Peng, Tian Nogal, Maria Casas, Joan R. Turmo, Jose Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method |
title | Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method |
title_full | Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method |
title_fullStr | Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method |
title_full_unstemmed | Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method |
title_short | Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method |
title_sort | role of sensors in error propagation with the dynamic constrained observability method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122277/ https://www.ncbi.nlm.nih.gov/pubmed/33919329 http://dx.doi.org/10.3390/s21092918 |
work_keys_str_mv | AT pengtian roleofsensorsinerrorpropagationwiththedynamicconstrainedobservabilitymethod AT nogalmaria roleofsensorsinerrorpropagationwiththedynamicconstrainedobservabilitymethod AT casasjoanr roleofsensorsinerrorpropagationwiththedynamicconstrainedobservabilitymethod AT turmojose roleofsensorsinerrorpropagationwiththedynamicconstrainedobservabilitymethod |