Abstract
This study proposes a deep-learning-based condition assessment approach by reconstructing marginal probability density distributions of girder vertical deflection (GVD) and cable tension (CT) using unsupervised image transformation (UNIT) model for a real cable-stayed bridge. 27 and 139 sensors of GVD and CT are distributed along the full bridge, and both the sampling frequencies are 2 Hz. First, vehicle-induced components of GVD and CT are extracted by data pre-processing while the temperature-induced components are removed, a time window of 3 h with a sliding length of 10 min is used to obtain original GVD and CT segments, and the corresponding margin probability density distributions (PDFs) are calculated by kernel density estimation. The training and test sets consist of 2986 and 6236 PDFs, respectively. The proposed UNIT model consists of a series of variational autoencoders (VAEs) and generative adversarial networks (GANs). Both the PDFs of GVD and CT are used as inputs. Then, the proposed UNIT model is updated by solving the mini-max problem of training GANs, in which VAEs act as generative models. Finally, the Wasserstein distance between the predicted and ground-truth PDFs of CT acts as the indicator of bridge condition change. Results show that specific modes of PDF variations induced by SHM system upgrade, cable damage, data anomaly, and traffic jam can be recognized to assess bridge condition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Worden, K., Manson, G.: The application of machine learning to structural health monitoring. Philos. Trans. 2007(365), 515 (1851)
Ding, Y.L., Deng, Y., Li, A.Q.: Study on correlations of modal frequencies and environmental factors for a suspension bridge based on improved neural networks. Sci. China-Technol. Sci. 53(9), 2501–2509 (2010)
Posenato, D., et al.: Model-free data interpretation for continuous monitoring of complex structures. Adv. Eng. Inform. 22(1), 135–144 (2008)
Niu, L., Cai, Q.: Structural health monitoring and damage detection using neural networks technique. In: 2013 Third International Conference on Intelligent System Design and Engineering Applications (Isdea), pp. 1302–1304 (2013)
Abdeljaber, O., et al.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 388, 154–170 (2017)
Nazarian, E., et al.: Detection of tension loss in cables of cable-stayed bridges by distributed monitoring of bridge deck strains. J. Struct. Eng. 142(6), 04016018 (2016)
Liu, M., Breuel, T.M., Kautz, J.: Unsupervised image-to-image translation networks. arXiv: Computer Vision and Pattern Recognition (2017)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv: Machine Learning (2013)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)
Zhu, J.-Y., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)
Li, S., et al.: Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio. Eng. Struct. 155, 1–15 (2018)
Acknowledgements
Financial support for this study was provided by the National Natural Science Foundation of China [Grant No. 51921006, 51638007, U1711265 and 52008138], National Key R&D Program of China [Grant No. 2019YFC1511102 and 2018YFC0705605], China Postdoctoral Science Foundation [Grant No. BX20190102 and 2019M661286], Heilongjiang Postdoctoral Funding [Grant No. LBH-TZ2016 and LBH-Z19064], Open Funding of State Key Laboratory of Safety and Health for In-service Long Bridges [Grant No. 2020SJKHIT001], and CCCC Science and Technology R&D Project [Grant No. 2018-ZJKJ-02].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, Y., Tian, Y., Zhang, Y., Li, H. (2021). Deep-Learning-Based Bridge Condition Assessment by Probability Density Distribution Reconstruction of Girder Vertical Deflection and Cable Tension Using Unsupervised Image Transformation Model. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2020. Lecture Notes in Civil Engineering, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-64908-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-64908-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64907-4
Online ISBN: 978-3-030-64908-1
eBook Packages: EngineeringEngineering (R0)