Simultaneous Recovery Model for Missing Multiple-Source Structural Health Monitoring Data of a Quayside Container Crane
Publication: Journal of Performance of Constructed Facilities
Volume 38, Issue 6
Abstract
Structural health monitoring (SHM) data encompasses vital information that provides a comprehensive understanding of the structural health condition. However, data loss may occur due to faults in acquisition equipment or sensors, and it is essential to reconstruct missing data to ensure the integrity of the monitoring information. Although extensive researches have been conducted on the topic of data recovery, a suitable missing data recovery method that can effectively address the missing data for multiple-source monitoring variables has not been identified yet. In this study, we proposed a novel missing data recovery model based on a deep learning framework to recover the missing strain and acceleration data simultaneously for SHM of the quayside container crane (QCC). The framework combines dual-tree complex wave transform (DTCWT) and bidirectional long short-term memory with attention mechanism (Att-BiLSTM). The multiple-source monitoring data are decomposed into several subtime series using the dual-tree complex wavelet, then, the Att-BiLSTM network is used to assign different weights to each subsequence in order to capture valuable information from the complete data. The effectiveness of the proposed model is verified by case studies, and the comparison results of missing data recovery under different miss rates show that the proposed model simultaneously improves the accuracy of missing data recovery for multiple-source monitoring variables.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work is supported by the Fundamental Research Funds for the Central Universities (Grant No. BLX202226).
References
Chen, B., T. Hu, Z. Huang, and C. Fang. 2018. “A spatio-temporal clustering and diagnosis method for concrete arch dams using deformation monitoring data.” Struct. Health Monit. 18 (5–6): 1355–1371. https://doi.org/10.1177/1475921718797949.
Chen, C., L. Tang, Y. Lu, L. Zhou, Z. Liu, Y. Liu, Z. Jiang, and B. Yang. 2023. “Temperature-induced response reconstruction method based on DL-AR model and attention mechanism.” Structures 50 (Apr): 359–372. https://doi.org/10.1016/j.istruc.2023.02.044.
Domala, V., and T. W. Kim. 2023. “Application of empirical mode decomposition and Hodrick Prescot filter for the prediction single step and multistep significant wave height with LSTM.” Ocean Eng. 285 (Oct): 115229. https://doi.org/10.1016/j.oceaneng.2023.115229.
Dong, G., H. Wan, Y. Luo, and M. D. Todd. 2023. “A fast sparsity-free compressive sensing approach for vibration data reconstruction using deep convolutional GAN.” Mech. Syst. Signal Process. 188 (Apr): 109937. https://doi.org/10.1016/j.ymssp.2022.109937.
Fan, G., Z. He, and J. Li. 2023. “Structural dynamic response reconstruction using self-attention enhanced generative adversarial networks.” Eng. Struct. 276 (Feb): 115334. https://doi.org/10.1016/j.engstruct.2022.115334.
Farhangfar, A., L. Kurgan, and J. Dy. 2008. “Impact of imputation of missing values on classification error for discrete data.” Pattern Recognit. 41 (12): 3692–3705. https://doi.org/10.1016/j.patcog.2008.05.019.
Gao, S., W. Zhao, C. Wan, H. Jiang, Y. Ding, and S. Xue. 2022. “Missing data imputation framework for bridge structural health monitoring based on slim generative adversarial networks.” Measurement 204 (Nov): 112095. https://doi.org/10.1016/j.measurement.2022.112095.
García-Laencina, P. J., J. Sancho-Gómez, A. R. Figueiras-Vidal, and M. Verleysen. 2009. “K nearest neighbours with mutual information for simultaneous classification and missing data imputation.” Neurocomputing 72 (7–9): 1483–1493. https://doi.org/10.1016/j.neucom.2008.11.026.
Gordan, M., S. Sabbagh-Yazdi, Z. Ismail, K. Ghaedi, P. Carroll, D. McCrum, and B. Samali. 2022. “State-of-the-art review on advancements of data mining in structural health monitoring.” Measurement 193 (Apr): 110939. https://doi.org/10.1016/j.measurement.2022.110939.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (Aug): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Jiang, H., C. Wan, K. Yang, Y. Ding, and S. Xue. 2022. “Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring.” Struct. Health Monit. 21 (3): 1093–1109. https://doi.org/10.1177/14759217211021942.
Kingsbury, N. 2001. “Complex wavelets for shift invariant analysis and filtering of signals.” Appl. Comput. Harmon. Anal. 10 (3): 234–253. https://doi.org/10.1006/acha.2000.0343.
Li, D., L. Li, X. Li, Z. Ke, and Q. Hu. 2020. “Smoothed LSTM-AE: A spatio-temporal deep model for multiple time-series missing imputation.” Neurocomputing 411 (Oct): 351–363. https://doi.org/10.1016/j.neucom.2020.05.033.
Li, Y., T. Bao, H. Chen, K. Zhang, X. Shu, Z. Chen, and Y. Hu. 2021. “A large-scale sensor missing data imputation framework for dams using deep learning and transfer learning strategy.” Measurement 178 (Oct): 109377. https://doi.org/10.1016/j.measurement.2021.109377.
Lin, Q., X. Bao, and C. Li. 2022. “Deep learning based missing data recovery of non-stationary wind velocity.” J. Wind Eng. Ind. Aerodyn. 224 (May): 104962. https://doi.org/10.1016/j.jweia.2022.104962.
Lu, Y., L. Tang, C. Chen, L. Zhou, Z. Liu, Y. Liu, Z. Jiang, and B. Yang. 2023. “Reconstruction of structural long-term acceleration response based on BiLSTM networks.” Eng. Struct. 285 (Jun): 116000. https://doi.org/10.1016/j.engstruct.2023.116000.
Luo, H., M. Huang, and Z. Zhou. 2019. “A dual-tree complex wavelet enhanced convolutional LSTM neural network for structural health monitoring of automotive suspension.” Measurement 137 (Apr): 14–27. https://doi.org/10.1016/j.measurement.2019.01.038.
Méndez, M., M. G. Merayo, and M. Núñez. 2023. “Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model.” Eng. Appl. Artif. Intell. 121 (May): 106041. https://doi.org/10.1016/j.engappai.2023.106041.
Niu, J., S. Li, and Z. Li. 2022. “Restoration of missing structural health monitoring data using spatiotemporal graph attention networks.” Struct. Health Monit. 21 (5): 2408–2419. https://doi.org/10.1177/14759217211056832.
Pan, Y., L. Zhang, X. Wu, K. Zhang, and M. J. Skibniewski. 2019. “Structural health monitoring and assessment using wavelet packet energy spectrum.” Saf. Sci. 120 (Dec): 652–665. https://doi.org/10.1016/j.ssci.2019.08.015.
Ranawat, N. S., J. Prakash, A. Miglani, and P. K. Kankar. 2023. “Performance evaluation of LSTM and Bi-LSTM using non-convolutional features for blockage detection in centrifugal pump.” Eng. Appl. Artif. Intell. 122 (Jun): 106092. https://doi.org/10.1016/j.engappai.2023.106092.
Sareen, K., B. K. Panigrahi, T. Shikhola, and R. Sharma. 2023. “An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction.” Energy 278 (Sep): 127799. https://doi.org/10.1016/j.energy.2023.127799.
Sun, Y., J. Li, Y. Xu, T. Zhang, and X. Wang. 2023. “Deep learning versus conventional methods for missing data imputation: A review and comparative study.” Expert Syst. Appl. 227 (Oct): 120201. https://doi.org/10.1016/j.eswa.2023.120201.
Tang, Z., Y. Bao, and H. Li. 2021. “Group sparsity-aware convolutional neural network for continuous missing data recovery of structural health monitoring.” Struct. Health Monit. 20 (4): 1738–1759. https://doi.org/10.1177/1475921720931745.
Tsai, C., M. Li, and W. Lin. 2018. “A class center based approach for missing value imputation.” Knowledge-Based Syst. 151 (Jul): 124–135. https://doi.org/10.1016/j.knosys.2018.03.026.
Vu, M. T., A. Jardani, N. Massei, and M. Fournier. 2021. “Reconstruction of missing groundwater level data by using long short-term memory (LSTM) deep neural network.” J. Hydrol. 597 (Jun): 125776. https://doi.org/10.1016/j.jhydrol.2020.125776.
Wang, Z., X. Lu, W. Zhang, V. C. Fragkoulis, M. Beer, and Y. Zhang. 2023. “Deep learning-based reconstruction of missing long-term girder-end displacement data for suspension bridge health monitoring.” Comput. Struct. 284 (Aug): 107070. https://doi.org/10.1016/j.compstruc.2023.107070.
Yi, T., H. Huang, and H. Li. 2017. “Development of sensor validation methodologies for structural health monitoring: A comprehensive review.” Measurement 109 (Oct): 200–214. https://doi.org/10.1016/j.measurement.2017.05.064.
Yu, X., W. Liang, L. Zhang, H. Jin, and J. Qiu. 2016. “Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline.” Mech. Syst. Signal Process. 72–73 (May): 266–285. https://doi.org/10.1016/j.ymssp.2015.10.034.
Zhang, W., F. Teng, J. Li, Z. Zhang, L. Niu, D. Zhang, Q. Song, and Z. Zhang. 2023. “Denoising method based on CNN-LSTM and CEEMD for LDV signals from accelerometer shock testing.” Measurement 216 (Jul): 112951. https://doi.org/10.1016/j.measurement.2023.112951.
Zhang, Z., and Y. Luo. 2017. “Restoring method for missing data of spatial structural stress monitoring based on correlation.” Mech. Syst. Signal Process. 91 (Jul): 266–277. https://doi.org/10.1016/j.ymssp.2017.01.018.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 38 • Issue 6 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Feb 8, 2024
Accepted: Jun 7, 2024
Published online: Aug 23, 2024
Published in print: Dec 1, 2024
Discussion open until: Jan 23, 2025
ASCE Technical Topics:
- Case studies
- Construction engineering
- Construction equipment
- Construction methods
- Container shipping
- Continuum mechanics
- Cranes
- Data collection
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Equipment and machinery
- Freight transportation
- Infrastructure
- Mathematical functions
- Mathematics
- Methodology (by type)
- Model accuracy
- Models (by type)
- Motion (dynamics)
- Oscillations
- Research methods (by type)
- Solid mechanics
- Structural engineering
- Structural health monitoring
- Transportation engineering
- Wavelets
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