Multivariate statistical analysis for early damage detection

•A data-driven strategy is proposed to efficiently detect early-damage on static data.•Damage features based on principal component analysis are introduced.•Combining symbolic data and clustering analysis enhances detection of early-damage.•Undamaged reference baselines were avoided by using only unsupervised methods.•A real-time SHM procedure was simulated to check the proposed strategy’s efficacy and sensitivity. 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%.