A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model
Abstract In the field of Structural Health Monitoring (SHM), complete datasets are fundamental for modal identification analysis and risk prediction. However, data loss due to sensor failures, transmission interruptions, or hardware issues is a common problem. To address this challenge, this study d...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-86781-7 |
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| author | Xiaoliang Jia Guoyan Zhang Zhiqiang Wang Huacong Li Jing Hu Songlin Zhu Caiwei Liu |
| author_facet | Xiaoliang Jia Guoyan Zhang Zhiqiang Wang Huacong Li Jing Hu Songlin Zhu Caiwei Liu |
| author_sort | Xiaoliang Jia |
| collection | DOAJ |
| description | Abstract In the field of Structural Health Monitoring (SHM), complete datasets are fundamental for modal identification analysis and risk prediction. However, data loss due to sensor failures, transmission interruptions, or hardware issues is a common problem. To address this challenge, this study develops a method combining Variational Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA)-optimized Gate Recurrent Unit (GRU) for recovering structural response data. The methodology initially employs Variational Mode Decomposition (VMD) to preprocess and decompose the existing data from the target sensor into Intrinsic Mode Functions (IMFs) and residuals. Subsequently, the Gated Recurrent Unit (GRU) network utilizes data from other sensors to reconstruct the IMFs and residuals, ultimately producing the data reconstruction results. During this process, Singular Spectrum Analysis (SSA) is used to optimize the hyperparameters of the GRU network. To validate the effectiveness of this method, we utilized one month of monitoring data collected from a certain project and a publicly available dataset. On the public dataset, we tested performance at different data loss rates. Results show that, compared to a standalone GRU model and a VMD + GRU model, the VMD + SSA + GRU model’s reconstruction data root mean squared error is reduced by 46.61% and 32.57% on average, respectively, while the coefficient of determination increases by 38.74% and 18.50%. The data reconstruction method proposed in this study can accurately capture trends in missing data, without the need for manual hyperparameter tuning, and the reconstruction results are highly consistent with the real data. |
| format | Article |
| id | doaj-art-04d1ca1689f34c4e80582dea5ca9fda5 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-04d1ca1689f34c4e80582dea5ca9fda52025-02-02T12:24:32ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-025-86781-7A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU modelXiaoliang Jia0Guoyan Zhang1Zhiqiang Wang2Huacong Li3Jing Hu4Songlin Zhu5Caiwei Liu6Shandong Lu Qiao Group CO., LTDShandong Lu Qiao Group CO., LTDShandong Lu Qiao Group CO., LTDShandong Lu Qiao Group CO., LTDShandong Zhengyuan Digital City Construction Co., LTDSchool of Civil Engineering, Qingdao University of TechnologySchool of Civil Engineering, Qingdao University of TechnologyAbstract In the field of Structural Health Monitoring (SHM), complete datasets are fundamental for modal identification analysis and risk prediction. However, data loss due to sensor failures, transmission interruptions, or hardware issues is a common problem. To address this challenge, this study develops a method combining Variational Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA)-optimized Gate Recurrent Unit (GRU) for recovering structural response data. The methodology initially employs Variational Mode Decomposition (VMD) to preprocess and decompose the existing data from the target sensor into Intrinsic Mode Functions (IMFs) and residuals. Subsequently, the Gated Recurrent Unit (GRU) network utilizes data from other sensors to reconstruct the IMFs and residuals, ultimately producing the data reconstruction results. During this process, Singular Spectrum Analysis (SSA) is used to optimize the hyperparameters of the GRU network. To validate the effectiveness of this method, we utilized one month of monitoring data collected from a certain project and a publicly available dataset. On the public dataset, we tested performance at different data loss rates. Results show that, compared to a standalone GRU model and a VMD + GRU model, the VMD + SSA + GRU model’s reconstruction data root mean squared error is reduced by 46.61% and 32.57% on average, respectively, while the coefficient of determination increases by 38.74% and 18.50%. The data reconstruction method proposed in this study can accurately capture trends in missing data, without the need for manual hyperparameter tuning, and the reconstruction results are highly consistent with the real data.https://doi.org/10.1038/s41598-025-86781-7Data ReconstructionStructural Health MonitoringGate Recurrent UnitVariational Mode DecompositionSparrow Search Algorithm |
| spellingShingle | Xiaoliang Jia Guoyan Zhang Zhiqiang Wang Huacong Li Jing Hu Songlin Zhu Caiwei Liu A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model Scientific Reports Data Reconstruction Structural Health Monitoring Gate Recurrent Unit Variational Mode Decomposition Sparrow Search Algorithm |
| title | A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model |
| title_full | A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model |
| title_fullStr | A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model |
| title_full_unstemmed | A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model |
| title_short | A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model |
| title_sort | structural health monitoring data reconstruction method based on vmd and ssa optimized gru model |
| topic | Data Reconstruction Structural Health Monitoring Gate Recurrent Unit Variational Mode Decomposition Sparrow Search Algorithm |
| url | https://doi.org/10.1038/s41598-025-86781-7 |
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