Abstract
To address the health monitoring challenges of bridge structures and identify suitable damage features, this paper initially denoises the input data using variational mode decomposition combined with wavelet threshold denoising. The multi-head self-attention mechanism is combined in parallel with the bidirectional long and short-term memory network to fully utilize the former’s capability to get the global features of the data and the latter’s capability to obtain the temporal features of the data. Ultimately, these two features are horizontally spliced to construct the parallel multi-head self-attention mechanism and bidirectional long and short-term memory (PMABL) network model. This paper uses the steel truss structure and IASC-ASCE benchmark datasets to assess the model. Experimental results demonstrate that the PMABL model surpasses existing models, achieving higher damage identification accuracy and better recognition ability for damage patterns with similar characteristics.
Similar content being viewed by others
References
Wang, L.; Yang, Z.; Waters, T.P.: Structural damage detection using cross correlation functions of vibration response. J. Sound Vib. 329(24), 5070–5086 (2010)
Wu, R.T.; Jahanshahi, M.R.: Data fusion approaches for structural health monitoring and system identification: past, present, and future. Struct. Health Monit. 19(2), 552–586 (2018)
Ye, X.; Jin, T.; Yun, C.: A review on deep learning-based structural health monitoring of civil infrastructures. Smart Struct. Syst. 24(5), 567–585 (2019)
Weinstein, J.C.; Masoud, S.; Brenner, B.R.: Bridge damage identification using artificial neural networks. J. Bridg. Eng. 23(11), 04018084 (2018)
Hoshyar, A.N.; Samali, B.; Liyanapathirana, R.; Houshyar, A.N.; Yu, Y.: Structural damage detection and localization using a hybrid method and artificial intelligence techniques. Struct. Health Monit. 19(5), 1507–1523 (2020)
Khoa, N.L.; Zhang, B.; Wang, Y.; Chen, F.; Mustapha, S.: Robust dimensionality reduction and damage detection approaches in structural health monitoring. Struct. Health Monit. 13(4), 406–417 (2014)
Jaime, V.; Francesc, P.; Diego, T.; Maribel, A.: A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications. Sensors 17(2), 417 (2017)
Chen, Z.; Zhang, R.; Zheng, J.; Sun, H.: Sparse Bayesian learning for structural damage identification. Mech. Syst. Signal Process. 140, 106689–110668914 (2020)
Lecun, Y.; Bottou, L.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Liu, T.; Xu, H.; Ragulskis, M.; Cao, M.; Ostachowicz, W.: A data-driven damage identification framework based on transmissibility function datasets and one-dimensional convolutional neural networks: verification on a structural health monitoring benchmark structure. Sensors 20(4), 1059 (2020)
Wang, X.; Zhang, X.; Shahzad, M.M.: A novel structural damage identification scheme based on deep learning framework. Structures 29, 1537–1549 (2021)
Zou, J.Z.; Yang, J.X.; Wang, G.P.; Tang, Y.L.; Yu, C.S.: Bridge structural damage identification based on parallel CNN-GRU. In: IOP Conference Series Earth and Environmental Science, vol. 626, p. 012017 (2021)
Yang, J.; Zhang, L.; Li, R.; He, Y.; Jiang, S.; Zou, J.: Research on bridge structural damage detection based on convolutional and long short-term memory neural networks. J. Railw. Sci. Eng. 17(8), 10 (2020)
Yang, J.; Zhang, L.; Chen, C.; Li, Y.; Zeng, Z.: A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection. Inf. Sci. 540, 117–130 (2020)
Yang, J.; Yang, F.; Zhou, Y.; Wang, D.; Chen, W.: A data-driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit. Inf. Sci. (2021)
Zhang, J.; Huang, C.; Wang, Z.: Research on structural damage identification based on multi-head self-attention and convolutional neural networks. J. Vib. Shock 41(24), 60–71 (2022)
Wang, W.; Su, C.: Automatic classification of reinforced concrete bridge defects using the hybrid network. Arab. J. Sci. Eng. 47(4), 5187–5197 (2022)
Schuster, M.; Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. (1997)
Dragomiretskiy, K.; Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2013)
Bentley, P.M.; McDonnell, J.: Wavelet transforms: an introduction. Electron. Commun. Eng. J. 6(4), 175–186 (1994)
Johnson, E.A.; Lam, H.-F.; Katafygiotis, L.S.; Beck, J.L.: Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data. J. Eng. Mech. 130(1), 3–15 (2004)
Graves, A.; Graves, A.: Long short-term memory. Supervised sequence labelling with recurrent neural networks, pp. 37–45 (2012)
Zhang, L.; Li, H.; Cui, J.; Wang, X.; Xiao, L.: An optimal variational mode decomposition method based on sparse index. J. Vib. Shock 42(8), 234–250 (2023)
Ba, J.L.; Kiros, J.R.; Hinton, G.E.: Layer Normalization (2016)
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, vol. 14, pp. 1137–1145, Montreal (1995).
Acknowledgements
We thank the editor and reviewers for their constructive suggestions for improving the work. We thank the Tianjin Municipal Science and Technology Program (Grant No. 23YDTPJC00350) for supporting this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, Q., Wang, J., Dai, H. et al. Bridge Structural Damage Identification Based on Parallel Multi-head Self-attention Mechanism and Bidirectional Long and Short-term Memory Network. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-09035-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13369-024-09035-0