Fuzzy clustering of time-series model to damage identification of structures. Issue 4 (March 2019)

• Record Type: Journal Article
• Authors: Zeng, Yongping; Yan, Yongyi; Weng, Shun; Sun, Yanhua; Tian, Wei; Yu, Hong
• Other Names: Li Yongle guest-editor.; Chen Jun guest-editor.; Xia Yong guest-editor.
• Is Part Of: Advances in structural engineering. Volume 22:Issue 4(2019)
• Page Start: 868
• Page End: 881
• Publication Date: 2019-03
• Subjects: auto-regressive model -- damage identification -- fuzzy clustering -- structural health monitoring -- time series; Structural engineering -- Periodicals; Construction, Technique de la; Structural engineering; Periodicals; 624.1
• DOI: 10.1177/1369433218789191
• Languages: English
• ISSNs: 1369-4332

Abstract

Time-series methods have been popularly used for damage identification of civil structure because of its output-only and non-model approach. Since the existence of structural damage is usually vague and not focussed on any particular time point, the switches in damage patterns from one time state to another are necessary to be treated in a fuzzy way. This article develops a damage identification method based on the fuzzy clustering of time-series model. The changes of model coefficients of time-series model are proposed to indicate the undamaged and damaged states by the fuzzy c-means clustering algorithm. The residual errors of time-series model are used to identify the damage location and damage severity. The proposed method is applied to an experimental segment lining and a numerical study of a practical bridge. The results verify that the proposed method is accurate and efficient to detect the structural damage location and severity. Since the computational process of time-series model and fuzzy clustering require low computational cost, the proposed data-based damage identification method is applicable to the online structural health monitoring system of large-scale civil structures.