Abstract
Structural Health Monitoring is one of the most promising and challenging areas of research in the field of Civil Engineering. Over the last decades, researchers have focused on the development of consistent and reliable indicators aiming to detect, locate, quantify or even predict damage. More recently, some researchers are focusing on the use of raw time histories extracted from structural dynamic monitoring to build damage indicators. In this sense, this paper has as its main interest the use of high-order statistics (HOS) coupled with clustering techniques i.e. the k-means and c-means algorithms to detect structural modification (damage). The approach is applied directly to dynamic measurements (accelerations) obtained on site. The efficiency of such methodology is attested by means of a numerical study performed on a model of a simply supported beam and a study based on a real case railway bridge, in France. Results show that HOS coupled with clustering techniques are able to differentiate damage scenarios with adequate classification rates.
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References
Alves, V., Cury, A., Roitman, N., Magluta, C., Cremona, C.: Structural modification assessment using supervised learning methods applied to vibration data. Eng. Struct. 99, 439–448 (2015)
Cury, A., Cremona, C.: Assignment of structural behaviors in long-term monitoring: application to a strengthened railway bridge. Struct. Health Monit. 11, 422–441 (2012)
Cury, A., Cremona, C., Diday, E.: Application of symbolic data analysis for structural modification assessment. Eng. Struct. 32(3), 762–775 (2010)
Santos, J.P., Cremona, C., Orcesi, A.D., Silveira, P.: Multivariate statistical analysis for early damage detection. Eng. Struct. 56, 273–285 (2013)
Madhulatha, T.S.: An overview on clustering methods. IOSR J. Eng. 2(4), 719–725 (2012)
Farrar, C., Worden, K.: Structural health monitoring: a machine learning perspective. Wiley, Chichester (2013)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum, New York (1981)
Alves, V., Cury, A., Roitman, N., Magluta, C., Cremona, C.: Novelty detection for SHM using raw acceleration measurements. Struct. Control Health Monit. 22, 1193–1207 (2015)
Acknowledgements
The authors would like to thank UFJF (Universidade Federal de Juiz de Fora - Federal University of Juiz de Fora), CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico - “National Council of Technological and Scisentific Development”) and FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais) for the financial support.
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Torres, A.S., Alves, V.N., Cury, A.A., Barbosa, F.S. (2018). Advanced Statistical Techniques Applied to Raw Data for Structural Damage Detection. In: Conte, J., Astroza, R., Benzoni, G., Feltrin, G., Loh, K., Moaveni, B. (eds) Experimental Vibration Analysis for Civil Structures. EVACES 2017. Lecture Notes in Civil Engineering , vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-67443-8_7
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DOI: https://doi.org/10.1007/978-3-319-67443-8_7
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