Fuzzy clustering of stability diagrams for vibration-based structural health monitoring

Abstract

A primary challenge to implementing structural health monitoring techniques on civil infrastructure is the differentiation of effects of environmental variables on the behaviour of structures from other causes of structural change. Data from the Z24 Bridge recorded over the course of nearly a year are analysed in this paper. Covariance-driven Stochastic Subspace Identification is applied to the data and a Fuzzy Clustering Algorithm is used to extract parameters indicative of the bridge’s state. The main benefit of this approach is the lack of need for mode shape information and thus it’s applicability to structures monitored with spatially sparse sensor grids. The method is shown to provide very encouraging results in separating the response data from the Z24 Bridge in normal and damaged states in varying environmental conditions, and the procedure is then applied to a second data set obtained from monitoring a tall building over several years of its early life in order to identify gradual or sudden structural changes.