Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods.

An important challenge in structural testing is accurately applying the prescribed boundary conditions and loading protocols on test specimens using hydraulic actuators to meet a given set of test objectives. Stability challenges are often encountered, particularly when testing stiff and brittle specimens where small displacements may produce large forces. This study addresses this issue by exploring an advanced control strategy for mixed-mode control to achieve simultaneous displacement and force control. A cascade control architecture is examined, where an inner proportional-integral controller manages rapid changes in forces while an outer proportional-integral controller loop is responsible for displacement tracking. A stability analysis is performed using a single degree-of-freedom (SDOF) specimen to illustrate the challenges and develop a procedure to design a mixed-mode controller. The performance of the controller is then examined by applying the developed procedure to two complex testing scenarios. The results indicate that this approach achieves good displacement tracking of a broadband signal and maintains stability during sudden stiffness drops, which are characteristic of brittle specimens. These findings demonstrate that a cascade control strategy is an appropriate alternative for structural testing such as quasi-static testing or hybrid simulation, especially for stiff specimens.