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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| Format: | Article |
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MDPI AG
2019-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/19/4341 |
| _version_ | 1828353280079560704 |
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| author | Cai Wu Shujin Li Yuanjin Zhang |
| author_facet | Cai Wu Shujin Li Yuanjin Zhang |
| author_sort | Cai Wu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T02:05:25Z |
| format | Article |
| id | doaj.art-12ab41d290d146c8851305d058803ebc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T02:05:25Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-12ab41d290d146c8851305d058803ebc2022-12-22T02:18:40ZengMDPI AGSensors1424-82202019-10-011919434110.3390/s19194341s19194341Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS SolutionCai Wu0Shujin Li1Yuanjin Zhang2School of Civil Engineering and Architecture, Wuhan University of Technology, Luoshi Road No.122, Wuhan 430070, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, Luoshi Road No.122, Wuhan 430070, ChinaSchool of Safety Science and Emergency Management, Wuhan University of Technology, Luoshi Road No.122, Wuhan 430070, ChinaStructural 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.https://www.mdpi.com/1424-8220/19/19/4341damage identificationauto-regressive modeltotal least-squares method |
| spellingShingle | Cai Wu Shujin Li Yuanjin Zhang Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution Sensors damage identification auto-regressive model total least-squares method |
| title | Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution |
| title_full | Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution |
| title_fullStr | Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution |
| title_full_unstemmed | Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution |
| title_short | Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution |
| title_sort | structural damage identification based on ar model with additive noises using an improved tls solution |
| topic | damage identification auto-regressive model total least-squares method |
| url | https://www.mdpi.com/1424-8220/19/19/4341 |
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