Structural damage detection based on semi-supervised fuzzy C-means clustering

Abstract

Structural damage detection is a key part of structural health monitoring. In recent years, intelligent detecting methods are used in this field and show good performance. This paper proposed a structural damage detection method based on data fusion and semi-supervised fuzzy C-means clustering. Compared with other intelligent method, our method can detect the damage location and extent, meanwhile, provide a confidence. Experiment results on a benchmark model show effectiveness of the proposed methods.