An Experimental Investigation for Detection, Localization, and Quantification of Compound Changes in Complex Uncertain Systems

Abstract

The field of (practical) data-driven approaches that utilize the vibration signature of target systems for developing mathematical models (for computational purposes, control, or anomaly detection for structural health monitoring) is still an active research area, despite the fact that several powerful system identification techniques have been developed in the system dynamics field to analyze such measurements. However, there is still a paucity of comprehensive experimental studies that investigate the range of validity of such identification techniques, particularly those applicable to realistic situations encountered in the structural engineering field, with the focus on detecting, quantifying, locating, and classifying observed changes, especially when there are significant inherent nonlinearities in the reference (undamaged) complex target structure, and where there are unavoidable sources of errors and uncertainties in the measurements and the attendant data analysis procedures. The research team constructed a well-instrumented, reconfigurable test apparatus (resembling a tall building) that allows the introduction of quantifiable levels of composite changes at various locations, orientations, and types of linear and/or nonlinear changes, with the aim of investigating a subset of the aforementioned challenges facing researchers who are interested in assessing the utility of some practical system identification approaches. The primary focus of the identification approach is on a decomposition procedure that is ideally suited for certain types of structures that possess some topological features that can be exploited to enhance the detectability of small changes. A companion paper provides a detailed description of the testbed features and its instrumentation. The present paper focuses on the analysis of some of the very extensive data sets that were created to study the usefulness of some practical dimensionless probabilistic measures that not only provide normalized change indices but also simultaneously attach a confidence level to each of these indices.