Research on ATT-BiLSTM-Based Restoration Method for Deflection Monitoring Data of a Steel Truss Bridge
Given the intricate operating environment of steel truss bridges, data anomalies are frequently initiated by faults in the sensor monitoring system itself during the monitoring process. This paper utilizes a steel truss bridge as a case study in engineering, with a primary focus on the deflection of...
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MDPI AG
2025-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8622 |
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| author | Yongjian Chen Rongzhen Liu Jianlin Wang Fan Pan Fei Lian Hui Cheng |
| author_facet | Yongjian Chen Rongzhen Liu Jianlin Wang Fan Pan Fei Lian Hui Cheng |
| author_sort | Yongjian Chen |
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| description | Given the intricate operating environment of steel truss bridges, data anomalies are frequently initiated by faults in the sensor monitoring system itself during the monitoring process. This paper utilizes a steel truss bridge as a case study in engineering, with a primary focus on the deflection of the main girder. The paper establishes an Attention Mechanism-based Bidirectional Long Short-Term Memory Neural Network (ATT-BiLSTM) model, with the objective of accurately repairing abnormal monitoring data. Firstly, correlation heat maps and Gray correlation are employed to detect anomalies in key measurement point data. Subsequently, the ATT-BiLSTM and Support Vector Machine (SVR) models are established to repair the anomalous monitoring data. Finally, various evaluation indexes, including Pearson’s correlation coefficient, mean squared error, and coefficient of determination, are utilized to validate the repairing accuracy of the ATT-BiLSTM model. The findings indicate that the repair efficacy of ATT-BiLSTM on anomalous data surpasses that of SVR. The repaired data exhibited a tendency to decrease in amplitude at the anomalous position, while maintaining the prominence of the data at abrupt deflection change points, thereby preserving the characteristics of the data. The repair rate of anomalous data attained 93.88%, and the mean square error of the actual complete data was only 0.0226, leading to substantial enhancement in the integrity and reliability of the data. |
| format | Article |
| id | doaj-art-b84682e5d31749adb9fbf75d0d7f2a08 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-b84682e5d31749adb9fbf75d0d7f2a082025-08-20T04:00:50ZengMDPI AGApplied Sciences2076-34172025-08-011515862210.3390/app15158622Research on ATT-BiLSTM-Based Restoration Method for Deflection Monitoring Data of a Steel Truss BridgeYongjian Chen0Rongzhen Liu1Jianlin Wang2Fan Pan3Fei Lian4Hui Cheng5College of Civil Engineering, Fuzhou University, Fuzhou 350108, ChinaCollege of Civil Engineering, Fuzhou University, Fuzhou 350108, ChinaCollege of Civil Engineering, Fuzhou University, Fuzhou 350108, ChinaFuzhou Zuohai Holding Group Co., Ltd., Fuzhou 350003, ChinaFuzhou Zuohai Holding Group Co., Ltd., Fuzhou 350003, ChinaRailway Bridge Science Research Institute, Ltd., Wuhan 430034, ChinaGiven the intricate operating environment of steel truss bridges, data anomalies are frequently initiated by faults in the sensor monitoring system itself during the monitoring process. This paper utilizes a steel truss bridge as a case study in engineering, with a primary focus on the deflection of the main girder. The paper establishes an Attention Mechanism-based Bidirectional Long Short-Term Memory Neural Network (ATT-BiLSTM) model, with the objective of accurately repairing abnormal monitoring data. Firstly, correlation heat maps and Gray correlation are employed to detect anomalies in key measurement point data. Subsequently, the ATT-BiLSTM and Support Vector Machine (SVR) models are established to repair the anomalous monitoring data. Finally, various evaluation indexes, including Pearson’s correlation coefficient, mean squared error, and coefficient of determination, are utilized to validate the repairing accuracy of the ATT-BiLSTM model. The findings indicate that the repair efficacy of ATT-BiLSTM on anomalous data surpasses that of SVR. The repaired data exhibited a tendency to decrease in amplitude at the anomalous position, while maintaining the prominence of the data at abrupt deflection change points, thereby preserving the characteristics of the data. The repair rate of anomalous data attained 93.88%, and the mean square error of the actual complete data was only 0.0226, leading to substantial enhancement in the integrity and reliability of the data.https://www.mdpi.com/2076-3417/15/15/8622steel truss bridgeATT-BiLSTMGray correlationdata restorationaccuracy assessmentSVR |
| spellingShingle | Yongjian Chen Rongzhen Liu Jianlin Wang Fan Pan Fei Lian Hui Cheng Research on ATT-BiLSTM-Based Restoration Method for Deflection Monitoring Data of a Steel Truss Bridge Applied Sciences steel truss bridge ATT-BiLSTM Gray correlation data restoration accuracy assessment SVR |
| title | Research on ATT-BiLSTM-Based Restoration Method for Deflection Monitoring Data of a Steel Truss Bridge |
| title_full | Research on ATT-BiLSTM-Based Restoration Method for Deflection Monitoring Data of a Steel Truss Bridge |
| title_fullStr | Research on ATT-BiLSTM-Based Restoration Method for Deflection Monitoring Data of a Steel Truss Bridge |
| title_full_unstemmed | Research on ATT-BiLSTM-Based Restoration Method for Deflection Monitoring Data of a Steel Truss Bridge |
| title_short | Research on ATT-BiLSTM-Based Restoration Method for Deflection Monitoring Data of a Steel Truss Bridge |
| title_sort | research on att bilstm based restoration method for deflection monitoring data of a steel truss bridge |
| topic | steel truss bridge ATT-BiLSTM Gray correlation data restoration accuracy assessment SVR |
| url | https://www.mdpi.com/2076-3417/15/15/8622 |
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