Damage identification of bridge structure model based on empirical mode decomposition algorithm and Autoregressive Integrated Moving Average procedure
Time series models have been used to extract damage features in the measured structural response. In order to better extract the sensitive features in the signal and detect structural damage, this paper proposes a damage identification method that combines empirical mode decomposition (EMD) and Auto...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2022-12-01
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| Series: | Archives of Civil Engineering |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/125700/PDF/art38_int.pdf |
| _version_ | 1797954375378796544 |
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| author | Weijia Lu Jiafan Dong Yuheng Pan Guoya Li Jinpeng Guo |
| author_facet | Weijia Lu Jiafan Dong Yuheng Pan Guoya Li Jinpeng Guo |
| author_sort | Weijia Lu |
| collection | DOAJ |
| description | Time series models have been used to extract damage features in the measured structural response. In order to better extract the sensitive features in the signal and detect structural damage, this paper proposes a damage identification method that combines empirical mode decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA) models. EMD decomposes nonlinear and non-stationary signals into different intrinsic mode functions (IMFs) according to frequency. IMF reduces the complexity of the signal and makes it easier to extract damage-sensitive features (DSF). The ARIMA model is used to extract damage sensitive features in IMF signals. The damage sensitive characteristic value of each node is used to analyze the location and damage degree of the damaged structure of the bridge. Considering that there are usually multiple failures in the actual engineering structure, this paper focuses on analysing the location and damage degree of multi-damaged bridge structures. A 6-meter-long multi-destructive steel-whole vibration experiment proved the state of the method. Meanwhile, the other two damage identification methods are compared. The results demonstrate that the DSF can effectively identify the damage location of the structure, and the accuracy rate has increased by 22.98% and 18.4% on average respectively. |
| first_indexed | 2024-04-10T23:17:39Z |
| format | Article |
| id | doaj.art-76c1d20a733c46c28918d02fb085b5a3 |
| institution | Directory Open Access Journal |
| issn | 1230-2945 |
| language | English |
| last_indexed | 2024-04-10T23:17:39Z |
| publishDate | 2022-12-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Civil Engineering |
| spelling | doaj.art-76c1d20a733c46c28918d02fb085b5a32023-01-12T18:06:08ZengPolish Academy of SciencesArchives of Civil Engineering1230-29452022-12-01vol. 68No 4653667https://doi.org/10.24425/ace.2022.143060Damage identification of bridge structure model based on empirical mode decomposition algorithm and Autoregressive Integrated Moving Average procedureWeijia Lu0https://orcid.org/0000-0002-0234-9412Jiafan Dong1https://orcid.org/0000-0002-6416-6910Yuheng Pan2https://orcid.org/0000-0003-3773-8810Guoya Li3https://orcid.org/0000-0003-3224-2824Jinpeng Guo4https://orcid.org/0000-0003-1705-2504Tianjin Chengjian University, Computer and Information Engineering Department, Tianjin, ChinaTianjin Chengjian University, Computer and Information Engineering Department, Tianjin, ChinaTianjin Chengjian University, Computer and Information Engineering Department, Tianjin, ChinaTianjin Chengjian University, Computer and Information Engineering Department, Tianjin, ChinaTianjin Chengjian University, Computer and Information Engineering Department, Tianjin, ChinaTime series models have been used to extract damage features in the measured structural response. In order to better extract the sensitive features in the signal and detect structural damage, this paper proposes a damage identification method that combines empirical mode decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA) models. EMD decomposes nonlinear and non-stationary signals into different intrinsic mode functions (IMFs) according to frequency. IMF reduces the complexity of the signal and makes it easier to extract damage-sensitive features (DSF). The ARIMA model is used to extract damage sensitive features in IMF signals. The damage sensitive characteristic value of each node is used to analyze the location and damage degree of the damaged structure of the bridge. Considering that there are usually multiple failures in the actual engineering structure, this paper focuses on analysing the location and damage degree of multi-damaged bridge structures. A 6-meter-long multi-destructive steel-whole vibration experiment proved the state of the method. Meanwhile, the other two damage identification methods are compared. The results demonstrate that the DSF can effectively identify the damage location of the structure, and the accuracy rate has increased by 22.98% and 18.4% on average respectively.https://journals.pan.pl/Content/125700/PDF/art38_int.pdfarima modelbridgedamage identificationemd algorithmmultiple damagestructural health monitoring |
| spellingShingle | Weijia Lu Jiafan Dong Yuheng Pan Guoya Li Jinpeng Guo Damage identification of bridge structure model based on empirical mode decomposition algorithm and Autoregressive Integrated Moving Average procedure Archives of Civil Engineering arima model bridge damage identification emd algorithm multiple damage structural health monitoring |
| title | Damage identification of bridge structure model based on empirical mode decomposition algorithm and Autoregressive Integrated Moving Average procedure |
| title_full | Damage identification of bridge structure model based on empirical mode decomposition algorithm and Autoregressive Integrated Moving Average procedure |
| title_fullStr | Damage identification of bridge structure model based on empirical mode decomposition algorithm and Autoregressive Integrated Moving Average procedure |
| title_full_unstemmed | Damage identification of bridge structure model based on empirical mode decomposition algorithm and Autoregressive Integrated Moving Average procedure |
| title_short | Damage identification of bridge structure model based on empirical mode decomposition algorithm and Autoregressive Integrated Moving Average procedure |
| title_sort | damage identification of bridge structure model based on empirical mode decomposition algorithm and autoregressive integrated moving average procedure |
| topic | arima model bridge damage identification emd algorithm multiple damage structural health monitoring |
| url | https://journals.pan.pl/Content/125700/PDF/art38_int.pdf |
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