Abstract
In most real-world applications, adverse influences caused by multiple sources of environmental variability conditions can mask extracted features and may lead to false indications of structural damage. Hence, it is thus fundamentally significant to investigate the effects of these variations on the damage-related features and damage detection procedure. This article proposes a new unsupervised machine learning technique for early damage detection of bridge structures, which are always exposed to environmental variability conditions. The proposed method is based on a data dependent dissimilarity measure. At last, the effectiveness and robustness of the proposed approach are assessed and verified through the well-known Tianjin-Yonghe Bridge; additionally, the proposed unsupervised machine learning methodology succeeds in early detecting damage under variability of environmental conditions.
Published Version
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