Hybrid weighted sequential learnong technique for structural health monitoring using learning approaches
DOI:
https://doi.org/10.56294/dm2025510Keywords:
Structural Health Monitoring, Damage Detection, Deep Learning, Finite Element Analysis, VibrationAbstract
Abstract- Structural Health Monitoring (SHM) plays a vital role in damage detection, offering significant maintenance and failure prevention benefits. Establishing effective SHM systems for damage identification (DI) traditionally requires extensive experimental datasets collected under varied operating and environmental conditions, which can be resource-intensive. This study introduces a novel approach to SHM by leveraging a Hybrid Weighted Sequential Learning Technique (HWSLT) classifier, which uses Finite Element (FE) computed responses to simulate structural behaviors under both healthy and damaged states. Initially, an optimal FE model representing a healthy, benchmark linear beam structure is developed and updated using experimental validation data. The HWSLT classifier is trained on SHM vibration data generated from this model under simulated load cases with uncertainty. This allows for minimal real-world experimental intervention while ensuring robust damage detection. Results demonstrate that the HWSLT classifier, trained with optimal FE model data, achieves high accuracy in predicting damage states in the benchmark structure, even when mixed with random disturbances. Conversely, data from non-ideal FE models produced unreliable classifications, underscoring the importance of model accuracy. These findings suggest that the integration of ideal FE models and deep learning offers a promising pathway for future SHM applications, with potential for reduced experimental costs and enhanced damage localization capabilities
References
1. Macleod, G. Dobie, S. G. Pierce, R. Summan, and M. Morozov, “Machining-based coverage path planning for automated structural inspection,” IEEE Trans. Autom. Sci. Eng., vol. 15, no. 1, pp. 202–213, Jan. 2018 DOI: https://doi.org/10.1109/TASE.2016.2601880
2. Entezami, H. Shariatmadar, and S. Mariani, “Fast unsupervised learning methods for structural health monitoring with large vibration data
3. Pnevmatikos, B. Blachowski, G. D. Hatzigeorgiou, and A. Swiercz, “Wavelet analysis based damage localization in steel frames with bolted connections,” Smart Struct. Syst., vol. 18, no. 6, pp. 1189–1202, Dec. 2016. DOI: https://doi.org/10.12989/sss.2016.18.6.1189
4. Zeng, Y. Liu, G. Zhang, L. Tang, Z. Jiang, and Z. Liu, “Analysis of structural responses of bridges based on long-term structural health monitoring,” Mech. Adv. Mater. Struc., vol. 25, no. 1, pp. 79–86, 2018. DOI: https://doi.org/10.1080/15376494.2016.1243283
5. Ma, S.-F. Jiang, C. Wu, and S.-Y. Wu, “Identification of sudden stiffness change in the acceleration response of a nonlinear hysteretic structure,” Shock Vib., vol. 2020, pp. 1–20, Jan. 2020. DOI: https://doi.org/10.1155/2020/3824216
6. Sadhu, “An integrated multivariate empirical mode decomposition method towards modal identification of structures,” J. Vib. Control, vol. 23, no. 17, pp. 2727–2741, Oct. 2017. DOI: https://doi.org/10.1177/1077546315621207
7. Tang, Z. Chen, Y. Bao, and H. Li, “Convolutional neural networkbased data anomaly detection method using multiple information for structural health monitoring,” Struct. Control Health Monitor., vol. 26, no. 1, p. e2296, Jan. 2019 DOI: https://doi.org/10.1002/stc.2296
8. Yang and R. Rai, “Machine auscultation: Enabling machine diagnostics using convolutional neural networks and large-scale machine audio data,” Adv. Manuf., vol. 7, no. 2, pp. 174–187, Jun. 2019 DOI: https://doi.org/10.1007/s40436-019-00254-5
9 .Virtanen et al., “Scipy 1.0: Fundamental algorithms for scientific computing in Python,” Nature Methods, vol. 17, pp. 261–272, Mar. 2020.
10. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, “1D convolutional neural networks and applications: A survey,” 2019, arXiv:1905.03554 DOI: https://doi.org/10.1109/ICASSP.2019.8682194
11. Paszke et al., “Pytorch: An imperative style, high-performance deep learning library,” in Proc. NeurIPS, 2019, pp. 8024–8035.
12. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 770–778 DOI: https://doi.org/10.1109/CVPR.2016.90
13 .Howard and S. Gugger, “Fastai: A layered API for deep learning,” Information, vol. 11, no. 2, p. 108, Feb. 2020. DOI: https://doi.org/10.3390/info11020108
14 .Li, C. Lam, J. Yao, and Q. Yao, “On testing for high-dimensional white noise,” Ann. Statist., vol. 47, no. 6, pp. 3382–3412, Dec. 2019. DOI: https://doi.org/10.1214/18-AOS1782
15.de Castro, F. G. Baptista, and F. Ciampa, “New signal processing approach for structural health monitoring in noisy environments based on impedance measurements,” Measurement, vol. 137, pp. 155–167, Apr. 2019. DOI: https://doi.org/10.1016/j.measurement.2019.01.054
16. Ibrahim, A. Eltawil, Y. Na, and S. El-Tawil, “A machine learning approach for structural health monitoring using noisy data sets,” IEEE Trans. Autom. Sci. Eng., vol. 17, no. 2, pp. 900–908, Apr. 2020 . 17. Tran-Ngoc et al., “An efficient approach to model updating for a multispan railway bridge using orthogonal diagonalization combined with improved particle swarm optimization,” J. Sound Vib., vol. 476, Jun. 2020 DOI: https://doi.org/10.1109/TASE.2019.2950958
18.Ho, T. Hoang, G. De Roeck, T. Bui, and M. Wahab, “Effects of measuring techniques on the accuracy of estimating cable tension in a cable-stay bridge,” in Proc. DAMAS, 2020, pp. 433–445. DOI: https://doi.org/10.1007/978-981-13-8331-1_31
19 .Farrar, C. R. & Worden, K. An introduction to structural health monitoring. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 365, 303–315 DOI: https://doi.org/10.1098/rsta.2006.1928
20. Güemes, A., Fernández-López, A., Díaz-Maroto, P. F., Lozano, A. & Sierra-Perez, J. Structural health monitoring in composite structures by fber-optic sensors. Sensors 18, 1094. https://doi.org/10.3390/s18041094 (2018). DOI: https://doi.org/10.3390/s18041094
21. Gulizzi, V., Rizzo, P., Milazzo, A. & LaMalfaRibolla, E. An integrated structural health monitoring system based on electromechanical impedance and guided ultrasonic waves. J. Civ. Struct. Health Monit. 5, 337–352. https://doi.org/10.1007/s13349-015-0112-0 (2015) DOI: https://doi.org/10.1007/s13349-015-0112-0
22. Dorafshan, S., Tomas, R. J. & Maguire, M. Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr. Build. Mater. 186, 1031–1045 (2018). DOI: https://doi.org/10.1016/j.conbuildmat.2018.08.011
23. Quqa, S. et al. Two-step approach for fatigue crack detection in steel bridges using convolutional neural networks. J. Civ. Struct. Health Monit. 12, 127–140 (2022). DOI: https://doi.org/10.1007/s13349-021-00537-1
24 .Bai, Y., Zha, B., Sezen, H. & Yilmaz, A. Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events. Struct. Health Monit. 14759, 217221083649 (2022). DOI: https://doi.org/10.1177/14759217221083649
25. Tenreiro, A. F. G., Lopes, A. M. & da Silva, L. F. A review of structural health monitoring of bonded structures using electromechanical impedance spectroscopy. Struct. Health Monit. 21, 228–249. https://doi.org/10.1177/1475921721993419 (2022). DOI: https://doi.org/10.1177/1475921721993419
26 .Fan, R. et al. Road crack detection using deep convolutional neural network and adaptive thresholding. In 2019 IEEE Intelligent Vehicles Symposium (IV), 474–479 (IEEE, 2019). DOI: https://doi.org/10.1109/IVS.2019.8814000
27. Boukellif, R. & Ricoeur, A. Identifcation of crack parameters and stress intensity factors in fnite and semi-infnite plates solving inverse problems of linear elasticity. Acta Mech. 231, 795–813. https://doi.org/10.1007/s00707-019-02575-9 (2020). DOI: https://doi.org/10.1007/s00707-019-02575-9
28. Chen, Y., Liu, Z.-Q. & Liu, H.-L. Parameters identifcation for crack in elastic structures based on fber bragg grating. Optik 154, 685–691. https://doi.org/10.1016/j.ijleo.2017.10.126 (2018). DOI: https://doi.org/10.1016/j.ijleo.2017.10.126
29 .Andersson, F., Hagqvist, A., Sundin, E. & Björkman, M. Design for manufacturing of composite structures for commercial aircrafthe development of a DFM strategy at SAAB aerostructures. Proced. Cirp 17, 362–367. https://doi.org/10.1016/j.procir.2014.02. 053 (2014) DOI: https://doi.org/10.1016/j.procir.2014.02.053
30. Azimi, M., Eslamlou, A. D. & Pekcan, G. Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review. Sensors 20, 2778. https://doi.org/10.3390/s20102778 (2020). DOI: https://doi.org/10.3390/s20102778
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Copyright (c) 2025 Dinesh Kumar Anguraj, Sivaneasan Bala Krishnan, T Sathish Kumar, Prasun Chakrabarti, Tulika Chakrabarti, Martin Margala, Siva Shankar S (Author)
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