Structural monitoring data repair based on a long short-term memory neural network
Abstract As construction technology and project management develop, structural monitoring systems become increasingly important for ensuring large-span spatial structure safety during construction and operation. However, most of the sensors and monitoring equipment in monitoring systems are poorly s...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-60196-2 |
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| _version_ | 1846158535027589120 |
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| author | Ba Panfeng Zhu Songlin Chai Hongyu Liu Caiwei Wu Pengtao Qi Lichang |
| author_facet | Ba Panfeng Zhu Songlin Chai Hongyu Liu Caiwei Wu Pengtao Qi Lichang |
| author_sort | Ba Panfeng |
| collection | DOAJ |
| description | Abstract As construction technology and project management develop, structural monitoring systems become increasingly important for ensuring large-span spatial structure safety during construction and operation. However, most of the sensors and monitoring equipment in monitoring systems are poorly serviced, resulting in frequent abnormal monitoring data, which directly leads to challenges in data analysis and structural safety assessment. In this paper, a structural response recovery method based on a long short-term memory (LSTM) neural network is proposed by studying the autocorrelation of data and the spatial correlations among data at multiple measurement points. The effectiveness and robustness of the proposed method are verified using the monitored stress data for a grid structure jacking construction process, and the influence of different data loss rates on the recovery accuracy is analysed. The recovery models are compared using a support vector machine and a Multi-Layer Perception (MLP) neural network. The proposed method can effectively restore missing data; notably, the MSE index is 0.6, and the MAPE is below 15%. The data restoration method based on the LSTM neural network is more accurate than the traditional method. Finally, the repair applicability of various types of monitored data is verified using the monitoring data from Hall F of Qingdao Jiao-dong International Airport under typhoon conditions. |
| format | Article |
| id | doaj-art-8bcc0231cf0e47f39b44520942398c1f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8bcc0231cf0e47f39b44520942398c1f2024-11-24T12:25:36ZengNature PortfolioScientific Reports2045-23222024-04-0114111810.1038/s41598-024-60196-2Structural monitoring data repair based on a long short-term memory neural networkBa Panfeng0Zhu Songlin1Chai Hongyu2Liu Caiwei3Wu Pengtao4Qi Lichang5School of Civil Engineering, Tianjin City Construction UniversitySchool of Civil Engineering, Qingdao University of TechnologySchool of Civil Engineering, Tianjin City Construction UniversitySchool of Civil Engineering, Qingdao University of TechnologySchool of Civil Engineering, Tianjin City Construction UniversityQingdao International Airport Group Co., LtdAbstract As construction technology and project management develop, structural monitoring systems become increasingly important for ensuring large-span spatial structure safety during construction and operation. However, most of the sensors and monitoring equipment in monitoring systems are poorly serviced, resulting in frequent abnormal monitoring data, which directly leads to challenges in data analysis and structural safety assessment. In this paper, a structural response recovery method based on a long short-term memory (LSTM) neural network is proposed by studying the autocorrelation of data and the spatial correlations among data at multiple measurement points. The effectiveness and robustness of the proposed method are verified using the monitored stress data for a grid structure jacking construction process, and the influence of different data loss rates on the recovery accuracy is analysed. The recovery models are compared using a support vector machine and a Multi-Layer Perception (MLP) neural network. The proposed method can effectively restore missing data; notably, the MSE index is 0.6, and the MAPE is below 15%. The data restoration method based on the LSTM neural network is more accurate than the traditional method. Finally, the repair applicability of various types of monitored data is verified using the monitoring data from Hall F of Qingdao Jiao-dong International Airport under typhoon conditions.https://doi.org/10.1038/s41598-024-60196-2Structural health monitoringData restorationLong short-term memory neural networkLarge-span structureSupport vector machines |
| spellingShingle | Ba Panfeng Zhu Songlin Chai Hongyu Liu Caiwei Wu Pengtao Qi Lichang Structural monitoring data repair based on a long short-term memory neural network Scientific Reports Structural health monitoring Data restoration Long short-term memory neural network Large-span structure Support vector machines |
| title | Structural monitoring data repair based on a long short-term memory neural network |
| title_full | Structural monitoring data repair based on a long short-term memory neural network |
| title_fullStr | Structural monitoring data repair based on a long short-term memory neural network |
| title_full_unstemmed | Structural monitoring data repair based on a long short-term memory neural network |
| title_short | Structural monitoring data repair based on a long short-term memory neural network |
| title_sort | structural monitoring data repair based on a long short term memory neural network |
| topic | Structural health monitoring Data restoration Long short-term memory neural network Large-span structure Support vector machines |
| url | https://doi.org/10.1038/s41598-024-60196-2 |
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