DAMAGE IDENTIFICATION THROUGH THE USE OF HIGHORDER STATISTICS

Autores

  • Alan Torres
  • Alexandre Cury

DOI:

https://doi.org/10.26512/ripe.v2i25.20840

Palavras-chave:

Damage detection. High-Order Statistics. Clustering methods. Raw.

Resumo

Structural Health Monitoring is based on the development of reliable and robust indicators capable to detect, locate, quantify and predict damage. Studies related to damage detection in civil engineering structures have a noticeable interest for researchers in this area. Indeed, the detection of structural changes likely to become critical can avoid the occurrence of major dysfunctions associated with social, economic and environmental consequences. Recently, many researchers have focused on dynamic assessment as part of structural diagnosis. Most of the studied techniques are based on time or frequency domain analyses to extract compressed information from modal characteristics or based on indicators built from these parameters. This work has as its main interest the use of highorder statistics (HOS) coupled with clustering techniques i.e. the k-means algorithm to detect structural modification (damage). The approach is applied directly to dynamic measurements (accelerations) obtained on site. In order to attest the efficiency of the proposed methodology,two investigations are carried out: a numerical model of a simply supported beam and a real case railway bridge, in France. It is shown that HOS coupled with clustering methods is able to distinguish structural conditions with adequate rates.

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Referências

Alves, V., Cury, A., Roitman, N., Magluta, C., Cremona, C., “Structural modification assessment using supervised learning methods applied to vibration data”, Engineering Structures 99, 439-448.

Cury, A., Cremona, C., Diday E., “Application of Symbolic Data Analysis for structural modification assessment”, Engineering Structures 2010, 32(3), 762-775.

Cury, A., Cremona, C., “Assignment of structural behaviors in long-term monitoring: Application to a strengthened railway bridge”. Structural Health Monitoring 2012, 1, 1-20.

Farrar, C., Worden, K, “Structural Health Monitoring: a machine learning perspective.” Chichester. Wiley. 2013.

Madhulatha, T.S., “An overview on clustering methods”, IOSR Journal of Engineering 2012, 2(4), 719-725.

Santos, J.P., Cremona, C., Orcesi, A.D., Silveira, P., “Multivariate statistical analysis for early damage detection”, Engineering Structures 2013, 56, 273-285.

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Publicado

2017-02-10

Como Citar

Torres, A., & Cury, A. (2017). DAMAGE IDENTIFICATION THROUGH THE USE OF HIGHORDER STATISTICS. Revista Interdisciplinar De Pesquisa Em Engenharia, 2(25), 20–28. https://doi.org/10.26512/ripe.v2i25.20840