Multivariate statistical analysis for early damage detection

João Pedro Santos (1) , Christian Cremona (2) , André Orcesi (3) , Paulo Silveira (1)

1 LNEC - Laboratório nacional de engenharia civil [Lisboa] = National Laboratory for Civil Engineering [Lisbon] = Laboratoire national de génie civil [Lisbonne]
2 Sétra
3 IFSTTAR/MAST/SDOA - Sécurité et Durabilité des Ouvrages d'Art

A large amount of researches and studies have been recently performed by applying statistical methods 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 in the combination of advanced multivariate statistical methods and quantities, such as principal components, symbolic data 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%.

Mots clés: SECURITE STRUCTURELLE; SURVEILLANCE; DOMMAGE MATERIEL; DETECTION; MODELE NUMERIQUE; SIMULATION; ANALYSE

Domaines: Mécanique des structures [physics.class-ph]; Mécanique des structures [physics.class-ph]; Vibrations [physics.class-ph]; Vibrations [physics.class-ph]

João Pedro Santos, Christian Cremona, André Orcesi, Paulo Silveira. Multivariate statistical analysis for early damage detection. Engineering Structures, 2013, 56, pp 273-285. ⟨10.1016/j.engstruct.2013.05.022⟩. ⟨hal-01063701⟩