Damage detection in a laboratory model using a nonlinear constraint satisfaction processor for finite element model updating

Physics-based approaches to vibration-based structural health monitoring largely rely on structural identification, or model updating, of a finite element model through correlation with the experimentally measured natural frequencies and mode shapes. Currently, the predominant technique used to perform model updating relies on local optimization strategies, such as gradient-based methods, or global optimization techniques, such as genetic algorithms. However, optimization-based approaches provide limited capabilities for addressing fundamental issues related to the inverse eigenvalue problem, including solution uniqueness, generation of alternative solutions in the presence of measurement uncertainties, and computational efficiency. Nonlinear constraint satisfaction processors with interval arithmetic have been recently explored by the authors as an alternative to optimization techniques for structural identification and provide unique and computationally swift capabilities for addressing these challenges. However, damage detection and diagnostics using structural identification by nonlinear constraint satisfaction processing with experimental data has yet to be explored. In this paper, a series of progressive damages are applied to an instrumented laboratory model of a multi-story shear building. Strategies for damage detection and quantification using finite element model updating with modal parameter estimates and reduced order analytical models are presented alongside results from application to the laboratory data.