Damage Detection of Beam Bridge Under a Moving Load Using Auto-encoder
Journal: Journal of Building Technology DOI: 10.32629/jbt.v3i1.423
Abstract
A novel damage detection approach based on Auto-encoder neural network is proposed to identify damage in beam-like bridges subjected to a moving mass. In this approach, several sensors are used to measure structural vibration responses during a mass moving across the bridge. An auto-encoder (AE) neural network is designed to extract features from the measured responses. A fixed moving window is used to cut out the time-domain responses to generate inputs of the AE neural network. Moreover, some constraints are applied on the hidden layer to improve the performance of the AE network in training process. When the training is complete, the encoder was regarded as a feature extractor. And the damage index is defined as the cosine distance between two feature vectors obtained from adjacent data windows. By moving the window along the measured vibration data, we can calculate a damage index series and locate the damage position of the structure. To demonstrate the performance of the proposed method, numerical simulation is carried out. The results show that the proposed method can accurately locate both single and multiple damages using acceleration response. It infers the proposed method is promising for structural damage detection.
Keywords
structural health monitoring; deep learning; auto-encoder; moving load; damage detection
Funding
The authors acknowledge the financial supports from the projects in key areas of Guangdong Province (No. 2019B111106001) and National Key Research and Development Project of China (No. 2019YFC1511000).
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[3] A.K Pandey, M. Biswas, M.M. Samman. (1991). Damage Detection from Changes in Curvature Mode Shapes. Sound Bib, 145(2): 321-332.
[4] Shi Z.Y., Law S.S., Zhang L.M. (2000). Structural Damage Localization from Modal Strain Energy Change. Journal of Sound and Vibration, 126(12): 825-844.
[5] F. Cavadas, I.F.C. Smith, J. Figueiras. (2013). Damage Detection Using Data-driven Methods Applied to Moving-load Responses. Mechanical Systems and Signal Processing, 39(1): 409-425.
[6] Bao C.X., Hao H., Li Z.X. (2013). Integrated ARMA Model Method for Damage Detection of Subsea Pipeline System. Engineering Structures, (48): 176-192.
[7] Hou, Z.K., Noori, M. N. Amand, R. S. (2000). Wavelet-based Approach for Structural Damage Detection. Journal of Engineering Mechanics, 126(7): 677-83.
[8] Ye X.W., Jin T., Yun C.B. (2019). A Review on Deep Learning-based Structural Health Monitoring of Civil Infrastructures. Smart Structures and Systems, 24(5): 567-585.
[9] Lin G., Wei J. (2010). Structural Damage Detection Based on BP Neural Network Technique. IEEE.
[10] Zhang J. (2011). Structural Damage Detection Using Parameters Combined with Changes in Flexibility Based on BP Neural Networks. Advanced Materials Research, 243-249: 5475-5480.
[11] Li J., U. Dackermann, Xu Y.L., etc. (2011). Damage Identification in Civil Engineering Structures Utilizing PCA-compressed Residual Frequency Response Functions and Neural Network Ensembles. Structural Control Health Monitoring, 18(2): 207-226.
[12] Mayu Sakurada, Takehisa yairi. (2014). Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction. Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, 4-11.
[13] Wang C., Wang B.L., Liu H.R., etc. (2020). Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network. Wireless Communication and Mobile Computing, (02): 17-35.
[14] Dong-wook, Ha, K., Ryu. (2017). Detecting Insider Threat Based on Machine Learning : Anomaly Detection Using RNN Autoencoder. Journal of the Korea Institute of Information Security & Cryptology, 27(4): 763-773.
[15] Ma X.R., Lin Y.Z., Nie Z.H. etc. (2020). Structural Damage Identification Based on Unsupervised Feature-extraction Via Variational Auto-encoder. Measurement, (160): 107811.
Copyright © 2021 Juntao Wu, Zhenhua Nie
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