A proposal of classification for machine-learning vibration-based damage identification methods
Francesca Marafini, Michele Betti, Gianni Bartoli, Giacomo Zini, Alberto Barontini, Nuno Mendes
download PDFAbstract. Recent advances in computing power and sensing technology led to a significant evolution of Structural Health Monitoring (SHM) techniques, transforming SHM into a “Big Data” problem. The use of data-driven approaches for damage identification purposes, specifically Machine Learning (ML) methods, has gained popularity. ML can help at various levels of the SHM process: to pre- and post-process input data, extract damage sensitive features, and operate pattern recognition in measured data and output valuable information for damage identification. In this paper, the role of ML in SHM applications is discussed together with a new scheme for classifying ML applications in SHM, especially focusing on vibration-based monitoring, given its consolidated theoretical base. Finally, the implications of the application of these methods to historic structures are discussed, with a brief account of existing case studies. The proposed classification is exemplified using the most recent studies available in the literature on cultural heritage structures.
Keywords
Machine Learning, Damage Identification, Vibration-Based SHM
Published online 3/17/2022, 6 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Francesca Marafini, Michele Betti, Gianni Bartoli, Giacomo Zini, Alberto Barontini, Nuno Mendes, A proposal of classification for machine-learning vibration-based damage identification methods, Materials Research Proceedings, Vol. 26, pp 593-598, 2023
DOI: https://doi.org/10.21741/9781644902431-96
The article was published as article 96 of the book Theoretical and Applied Mechanics
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] C. Cremona and J. Santos, “Structural health monitoring as a big-data problem,” Struct. Eng. Int., vol. 28, no. 3, pp. 243–254, 2018. https://doi.org/10.1080/10168664.2018.1461536
[2] O. Avci, O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj, and D. J. Inman, “A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications,” Mech. Syst. Signal Process., vol. 147, p. 107077, 2021. https://doi.org/10.1016/j.ymssp.2020.107077
[3] H.-T. Thai, “Machine learning for structural engineering: A state-of-the-art review,” Structures, vol. 38, no. December 2021, pp. 448–491, 2022. https://doi.org/10.1016/j.istruc.2022.02.003
[4] X. W. Ye, T. Jin, and C. B. Yun, “A review on deep learning-based structural health monitoring of civil infrastructures,” Smart Struct. Syst., vol. 24, no. 5, pp. 567–585, 2019. https://doi.org/10.12989/sss.2019.24.5.567.
[5] M. Mishra, “Machine learning techniques for structural health monitoring of heritage buildings: A state-of-the-art review and case studies,” J. Cult. Herit., vol. 47, pp. 227–245, 2021. https://doi.org/10.1016/j.culher.2020.09.005
[6] K. Worden, C. R. Farrar, G. Manson, and G. Park, “The fundamental axioms of structural health monitoring,” Proc. R. Soc. A Math. Phys. Eng. Sci., vol. 463, no. 2082, pp. 1639–1664. https://doi.org/10.1098/rspa.2007.1834
[7] R. J. Barthorpe, “On Model and Data Based Approaches to Structural Health Monitoring,” 2010.
[8] Y. Hamishebahar, H. Z. Li, and H. Guan, “Application of machine learning algorithms in structural health monitoring research,” Lect. Notes Civ. Eng., vol. 101, pp. 219–228, 2021. https://doi.org/10.1007/978-981-15-8079-6_21
[9] A. Malekloo, E. Ozer, M. AlHamaydeh, and M. Girolami, Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights, vol. 0, no. 0. 2021. https://doi.org/10.1177/14759217211036880
[10] H. Salehi and R. Burgueño, “Emerging artificial intelligence methods in structural engineering,” Eng. Struct., vol. 171, no. April, pp. 170–189, 2018. https://doi.org/10.1016/j.engstruct.2018.05.084
[11] D. A. Tibaduiza Burgos, R. C. Gomez Vargas, C. Pedraza, D. Agis, and F. Pozo, Damage identification in structural health monitoring: A brief review from its implementation to the use of data-driven applications, vol. 20, no. 3. 2020. https://doi.org/10.3390/s20030733
[12] C. R. Farrar and K. Worden, “Structural Health Monitoring: A Machine Learning Perspective,” Struct. Heal. Monit. A Mach. Learn. Perspect., 2012. https://doi.org/10.1002/9781118443118
[13] C. M. Bishop, “Pattern Recoginiton and Machine Learning,” Inf. Sci. Stat., p. 738, 2006, Accessed: Jul. 03, 2022.
[14] M. Gordan et al., “State-of-the-art review on advancements of data mining in structural health monitoring,” Meas. J. Int. Meas. Confed., vol. 193, no. February, 2022. https://doi.org/10.1016/j.measurement.2022.110939
[15] A. Rytter, “Vibrational Based Inspection of Civil Engineering Structures.,” Aalborg University, 1993.
[16] D. M. Bourdalos, I. A. Iliopoulos, and J. S. S. B, “On the Detection of Incipient Faults in Rotating Machinery Under Different Operating Speeds Using Unsupervised Vibration-Based Statistical Time Series,” no. Cm, pp. 287–296. https://doi.org/10.1007/978-3-031-07258-1_30
[17] F. Clementi, A. Formisano, G. Milani, and F. Ubertini, “Structural Health Monitoring of Architectural Heritage: From the past to the Future Advances,” Int. J. Archit. Herit., vol. 15, no. 1, pp. 1–4, 2021. https://doi.org/10.1080/15583058.2021.1879499
[18] G. Marrongelli, R. Finotti, C. Gentile, and F. Barbosa, “An artificial intelligence strategy to detect damage from response measurements: application on an ancient tower,” MATEC Web Conf., vol. 211, p. 21002, Oct. 2018. https://doi.org/10.1051/matecconf/201821121002
[19] E. K. Chalouhi, I. Gonzalez, C. Gentile, and R. Karoumi, “Damage detection in railway bridges using Machine Learning: application to a historic structure,” Procedia Eng., vol. 199, pp. 1931–1936, Jan. 2017. https://doi.org/10.1016/j.proeng.2017.09.287
[20] J. Gosliga, P. A. Gardner, L. A. Bull, N. Dervilis, and K. Worden, “Foundations of Population-based SHM, Part {II}: Heterogeneous populations–Graphs, networks, and communities,” Mech. Syst. Signal Process., vol. 148, p. 107144, 2021. https://doi.org/10.1016/j.ymssp.2020.107144
[21] E. J. Cross, S. J. Gibson, M. R. Jones, D. J. Pitchforth, S. Zhang, and T. J. Rogers, “Physics-Informed Machine Learning for Structural Health Monitoring,” Struct. Integr., vol. 21, pp. 347–367, 2022. https://doi.org/10.1007/978-3-030-81716-9_17
