Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution

Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR m...
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Autor*in:	Cai Wu [verfasserIn] Shujin Li [verfasserIn] Yuanjin Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	damage identification auto-regressive model total least-squares method

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 19(2019), 19, p 4341
Übergeordnetes Werk:	volume:19 ; year:2019 ; number:19, p 4341

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s19194341

Katalog-ID:	DOAJ030149983

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520			\|a Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLS<sub<E</sub<) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLS<sub<E</sub< method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure.
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allfields	10.3390/s19194341 doi (DE-627)DOAJ030149983 (DE-599)DOAJ12ab41d290d146c8851305d058803ebc DE-627 ger DE-627 rakwb eng TP1-1185 Cai Wu verfasserin aut Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLS<sub<E</sub<) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLS<sub<E</sub< method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure. damage identification auto-regressive model total least-squares method Chemical technology Shujin Li verfasserin aut Yuanjin Zhang verfasserin aut In Sensors MDPI AG, 2003 19(2019), 19, p 4341 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:19, p 4341 https://doi.org/10.3390/s19194341 kostenfrei https://doaj.org/article/12ab41d290d146c8851305d058803ebc kostenfrei https://www.mdpi.com/1424-8220/19/19/4341 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 19, p 4341
spelling	10.3390/s19194341 doi (DE-627)DOAJ030149983 (DE-599)DOAJ12ab41d290d146c8851305d058803ebc DE-627 ger DE-627 rakwb eng TP1-1185 Cai Wu verfasserin aut Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLS<sub<E</sub<) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLS<sub<E</sub< method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure. damage identification auto-regressive model total least-squares method Chemical technology Shujin Li verfasserin aut Yuanjin Zhang verfasserin aut In Sensors MDPI AG, 2003 19(2019), 19, p 4341 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:19, p 4341 https://doi.org/10.3390/s19194341 kostenfrei https://doaj.org/article/12ab41d290d146c8851305d058803ebc kostenfrei https://www.mdpi.com/1424-8220/19/19/4341 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 19, p 4341
allfields_unstemmed	10.3390/s19194341 doi (DE-627)DOAJ030149983 (DE-599)DOAJ12ab41d290d146c8851305d058803ebc DE-627 ger DE-627 rakwb eng TP1-1185 Cai Wu verfasserin aut Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLS<sub<E</sub<) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLS<sub<E</sub< method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure. damage identification auto-regressive model total least-squares method Chemical technology Shujin Li verfasserin aut Yuanjin Zhang verfasserin aut In Sensors MDPI AG, 2003 19(2019), 19, p 4341 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:19, p 4341 https://doi.org/10.3390/s19194341 kostenfrei https://doaj.org/article/12ab41d290d146c8851305d058803ebc kostenfrei https://www.mdpi.com/1424-8220/19/19/4341 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 19, p 4341
allfieldsGer	10.3390/s19194341 doi (DE-627)DOAJ030149983 (DE-599)DOAJ12ab41d290d146c8851305d058803ebc DE-627 ger DE-627 rakwb eng TP1-1185 Cai Wu verfasserin aut Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLS<sub<E</sub<) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLS<sub<E</sub< method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure. damage identification auto-regressive model total least-squares method Chemical technology Shujin Li verfasserin aut Yuanjin Zhang verfasserin aut In Sensors MDPI AG, 2003 19(2019), 19, p 4341 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:19, p 4341 https://doi.org/10.3390/s19194341 kostenfrei https://doaj.org/article/12ab41d290d146c8851305d058803ebc kostenfrei https://www.mdpi.com/1424-8220/19/19/4341 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 19, p 4341
allfieldsSound	10.3390/s19194341 doi (DE-627)DOAJ030149983 (DE-599)DOAJ12ab41d290d146c8851305d058803ebc DE-627 ger DE-627 rakwb eng TP1-1185 Cai Wu verfasserin aut Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLS<sub<E</sub<) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLS<sub<E</sub< method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure. damage identification auto-regressive model total least-squares method Chemical technology Shujin Li verfasserin aut Yuanjin Zhang verfasserin aut In Sensors MDPI AG, 2003 19(2019), 19, p 4341 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:19, p 4341 https://doi.org/10.3390/s19194341 kostenfrei https://doaj.org/article/12ab41d290d146c8851305d058803ebc kostenfrei https://www.mdpi.com/1424-8220/19/19/4341 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 19, p 4341
language	English
source	In Sensors 19(2019), 19, p 4341 volume:19 year:2019 number:19, p 4341
sourceStr	In Sensors 19(2019), 19, p 4341 volume:19 year:2019 number:19, p 4341
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	damage identification auto-regressive model total least-squares method Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Cai Wu @@aut@@ Shujin Li @@aut@@ Yuanjin Zhang @@aut@@
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callnumber-first	T - Technology
author	Cai Wu
spellingShingle	Cai Wu misc TP1-1185 misc damage identification misc auto-regressive model misc total least-squares method misc Chemical technology Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution
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topic_title	TP1-1185 Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution damage identification auto-regressive model total least-squares method
topic	misc TP1-1185 misc damage identification misc auto-regressive model misc total least-squares method misc Chemical technology
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title	Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution
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title_sort	structural damage identification based on ar model with additive noises using an improved tls solution
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title_auth	Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution
abstract	Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLS<sub<E</sub<) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLS<sub<E</sub< method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure.
abstractGer	Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLS<sub<E</sub<) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLS<sub<E</sub< method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure.
abstract_unstemmed	Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLS<sub<E</sub<) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLS<sub<E</sub< method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure.
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title_short	Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution
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