Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health Monitoring
For structural health monitoring (SHM), a complete dataset is crucial for further modal identification analysis and risk warning. Unfortunately, data loss can occur due to sensor failure, transmission system interruption, or hardware failure, which can lead to missing data. Therefore, this study pro...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/14/1/251 |
| _version_ | 1827369710414462976 |
|---|---|
| author | Songlin Zhu Jijun Miao Wei Chen Caiwei Liu Chengliang Weng Yichun Luo |
| author_facet | Songlin Zhu Jijun Miao Wei Chen Caiwei Liu Chengliang Weng Yichun Luo |
| author_sort | Songlin Zhu |
| collection | DOAJ |
| description | For structural health monitoring (SHM), a complete dataset is crucial for further modal identification analysis and risk warning. Unfortunately, data loss can occur due to sensor failure, transmission system interruption, or hardware failure, which can lead to missing data. Therefore, this study proposes a bidirectional long short-term memory neural network (Bi-LSTM) response recovery method based on variational mode decomposition (VMD) and sparrow search algorithm (SSA) optimization that utilizes the structural response data between multiple sensors and can simultaneously consider temporal and spatial correlations. A dataset containing approximately half a month of monitoring data was collected from a certain project for training, validation, and testing. A publicly available dataset was also referenced to validate the proposed method in this paper. Using the public dataset, under 13 different data loss rates, the VMD + SSA + Bi-LSTM model reduced the RMSE of data reconstruction by an average of 65.01% and 45.35% compared to the Bi-LSTM model and the VMD + Bi-LSTM models, respectively, while the coefficient of determination increased by 62.21% and 11.19%. The data reconstruction method proposed in this paper can accurately reconstruct the variation trends of missing data without the manual optimization of hyperparameters, and the reconstruction results are close to the real data. |
| first_indexed | 2024-03-08T09:56:25Z |
| format | Article |
| id | doaj.art-fbcfac3fc96d4f159db9748c8b647272 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-08T09:56:25Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-fbcfac3fc96d4f159db9748c8b6472722024-01-29T13:49:31ZengMDPI AGBuildings2075-53092024-01-0114125110.3390/buildings14010251Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health MonitoringSonglin Zhu0Jijun Miao1Wei Chen2Caiwei Liu3Chengliang Weng4Yichun Luo5School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Civil Engineering, Qingdao University of Technology, Qingdao 266520, ChinaJiangsu Key Laboratory of Environmental Impact and Structural Safety in Engineering, China University of Mining & Technology, Xuzhou 221116, ChinaSchool of Civil Engineering, Qingdao University of Technology, Qingdao 266520, ChinaShandong Luqiao Group Co., Ltd., Jinan 250021, ChinaShandong Luqiao Group Co., Ltd., Jinan 250021, ChinaFor structural health monitoring (SHM), a complete dataset is crucial for further modal identification analysis and risk warning. Unfortunately, data loss can occur due to sensor failure, transmission system interruption, or hardware failure, which can lead to missing data. Therefore, this study proposes a bidirectional long short-term memory neural network (Bi-LSTM) response recovery method based on variational mode decomposition (VMD) and sparrow search algorithm (SSA) optimization that utilizes the structural response data between multiple sensors and can simultaneously consider temporal and spatial correlations. A dataset containing approximately half a month of monitoring data was collected from a certain project for training, validation, and testing. A publicly available dataset was also referenced to validate the proposed method in this paper. Using the public dataset, under 13 different data loss rates, the VMD + SSA + Bi-LSTM model reduced the RMSE of data reconstruction by an average of 65.01% and 45.35% compared to the Bi-LSTM model and the VMD + Bi-LSTM models, respectively, while the coefficient of determination increased by 62.21% and 11.19%. The data reconstruction method proposed in this paper can accurately reconstruct the variation trends of missing data without the manual optimization of hyperparameters, and the reconstruction results are close to the real data.https://www.mdpi.com/2075-5309/14/1/251data reconstructionstructural health monitoringBi-LSTMVMDSSA |
| spellingShingle | Songlin Zhu Jijun Miao Wei Chen Caiwei Liu Chengliang Weng Yichun Luo Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health Monitoring Buildings data reconstruction structural health monitoring Bi-LSTM VMD SSA |
| title | Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health Monitoring |
| title_full | Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health Monitoring |
| title_fullStr | Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health Monitoring |
| title_full_unstemmed | Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health Monitoring |
| title_short | Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health Monitoring |
| title_sort | reconstructing missing data using a bi lstm model based on vmd and ssa for structural health monitoring |
| topic | data reconstruction structural health monitoring Bi-LSTM VMD SSA |
| url | https://www.mdpi.com/2075-5309/14/1/251 |
| work_keys_str_mv | AT songlinzhu reconstructingmissingdatausingabilstmmodelbasedonvmdandssaforstructuralhealthmonitoring AT jijunmiao reconstructingmissingdatausingabilstmmodelbasedonvmdandssaforstructuralhealthmonitoring AT weichen reconstructingmissingdatausingabilstmmodelbasedonvmdandssaforstructuralhealthmonitoring AT caiweiliu reconstructingmissingdatausingabilstmmodelbasedonvmdandssaforstructuralhealthmonitoring AT chengliangweng reconstructingmissingdatausingabilstmmodelbasedonvmdandssaforstructuralhealthmonitoring AT yichunluo reconstructingmissingdatausingabilstmmodelbasedonvmdandssaforstructuralhealthmonitoring |