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Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

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  • Special Column on Civil Structure Vibration Based Health and Safety Monitoring
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Abstract

A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character.

The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis.

From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.

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Author information

Authors and Affiliations

  1. Department of Structures, Av. Brasil 101, Lisbon, 1700-066, Portugal

    João Pedro Santos & Paulo Silveira

  2. Technical Center for Bridge Engineering, CEREMA, B.P. 214, Provins Cedex, 77487, France

    Christian Cremona

  3. Materials and Structures Department, IFSTTAR, University Paris-Est, 14-20 Boulevard Newton, Champs sur Marne, Marne la Vallée Cedex2, F-77447, France

    André D. Orcesi

  4. Department of Civil Engineering, Technical University Lisbon, Avenida Rovisco Pais, Lisbon, 1096, Portugal

    Luis Calado

Authors
  1. João Pedro Santos
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  2. Christian Cremona
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  3. André D. Orcesi
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  4. Paulo Silveira
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  5. Luis Calado
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Corresponding author

Correspondence to Christian Cremona.

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Santos, J.P., Cremona, C., Orcesi, A.D. et al. Static-based early-damage detection using symbolic data analysis and unsupervised learning methods. Front. Struct. Civ. Eng. 9, 1–16 (2015). https://doi.org/10.1007/s11709-014-0277-3

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  • DOI: https://doi.org/10.1007/s11709-014-0277-3

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