A structural damage detection technique based on agglomerative clustering applied to the Z 24 Bridge
In the last decades, the structural health monitoring (SHM) has emerged as a promising approach to provide more reliable and quantitative knowledge about structural condition. Posed in the context of a statistical pattern recognition paradigm, the SHM has evolved and assigned, for instance, to the continuous development of machine learning algorithms that model the normal condition of a structure and, automatically, allow to infer early signs of damage. To overcome the unknown effects of operational and environmental variability, new cluster based algorithms have been required due to their inherent capability to automatically discover the main structural components of a system, allowing a proper learning of the normal condition. Therefore, this paper proposes a novel unsupervised cluster based technique, the agglomerative concentric hypersphere (ACH) to learn the structural response as a small number of structural states, even when operational an environmental influences produce nonlinear effects in the data. Through a simple initialization and an agglomerative clustering procedure, this technique assumes no underlying data distribution and does not require input parameters. The ACH is compared to the Mahalanobis squared distance algorithm on standard data sets from a post tensioned bridge: the Z 24 Bridge. The proposed technique demonstrates to be more suitable to model the normal condition of the test structure as main structural components. Furthermore, the technique reveals a better classification performance than the alternative one in terms of false positive and false negative indications of damage, demonstrating its applicability for real SHM scenarios.