Agglomerative concentric hypersphere clustering applied to structural damage detection. (August 2017)

• Record Type: Journal Article
• Authors: Silva, Moisés; Santos, Adam; Santos, Reginaldo; Figueiredo, Eloi; Sales, Claudomiro; Costa, João C.W.A.
• Is Part Of: Mechanical systems and signal processing. Volume 92(2017)
• Page Start: 196
• Page End: 212
• Publication Date: 2017-08
• Subjects: Agglomerative concentric hypersphere -- Clustering -- Damage detection -- Structural health monitoring -- Environmental conditions -- Operational conditions; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.01.024
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A novel clustering algorithm for structural damage detection under normal conditions. A data density-based approach to automatically estimate the number of clusters. The algorithm is output-only, nonparametric and does not require any input parameter. Improved and reliable results if compared to state-of-the-art techniques. The approach is indicated when life-safety and economical factors are the main goals. Abstract: The present paper proposes a novel cluster-based method, named as agglomerative concentric hypersphere (ACH), to detect structural damage in engineering structures. Continuous structural monitoring systems often require unsupervised approaches to automatically infer the health condition of a structure. However, when a structure is under linear and nonlinear effects caused by environmental and operational variability, data normalization procedures are also required to overcome these effects. The proposed approach aims, through a straightforward clustering procedure, to discover automatically the optimal number of clusters, representing the main state conditions of a structural system. Three initialization procedures are introduced to evaluate the impact of deterministic and stochastic initializations on the performance of this approach. The ACH is compared to state-of-the-art approaches, based on Gaussian mixture models and Mahalanobis squared distance, on standard data sets from a post-tensioned bridge located in Switzerland: the Z-24 Bridge. The proposed approach demonstrates more efficiency in modeling the normal condition of the structure and its corresponding main clusters. Furthermore, it reveals a better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, demonstrating a promising applicability in real-world structural health monitoring scenarios.