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.

Structural health monitoring of offshore wind turbines using automated operational modal analysis

“…This is followed for instance in Scionti et al 15 An alternative is the use of cluster analysis. 16 This can be performed using non-hierarchical clustering algorithms, as it is presented in Verboven et al, 13 Goethals et al 17 and Carden and Brownjohn; 18 or using hierarchical algorithms, as it is described in Magalha˜es et al 19 and Verboven et al 20 …”

Vibration-Based Monitoring and Diagnosis of Cultural Heritage: A Methodological Discussion in Three Examples

“…By executing a statistical recurrence of the system's natural frequencies identified by the SSI algorithm and by averaging the values, it proves possible to distinguish between the real modes of the structure and modes that appear occasionally, and might be possibly due to exogenous components (Carden and Brownjohn, 2008).…”

Phase Space Dissimilarity Measures for Structural Health Monitoring

“…This sensitivity causes an excess of false positives (Worden 2006). Discrimination between environmental and damage effects via modal frequencies has been demonstrated by covariance-driven stochastic subspace identification with 'fuzzy clustering' algorithm (Carden 2008).…”