The present paper proposes a novel cluster-basedmethod, 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.(C) 2017 Elsevier Ltd. All rights reserved.