Charles R. Farrar

TL;DR: The work explores estimating AE signal statistics in the compressed domain for low-power classification applications and investigates the suitability of compressed sensing techniques for AE-based SHM applications.

TL;DR: In this article, a robust and cost-effective monitoring system for welded beam-column connections in a moment resisting frame structure is proposed, which integrates structural dynamics, wireless data acquisition, local actuation, micro-electromechanical systems (MEMS) technology, and statistical pattern recognition algorithms.

TL;DR: In this paper, the stiffness of reinforced concrete shear wall elements was evaluated statically, dynamically, and with simulated seismic base excitations on a shake table; and with experimental modal analysis procedures.

Abstract: This paper focuses on applying statistical process control techniques to vibration-based damage diagnosis. First, an auto-regressive (AR) model is fit to the measured acceleration-time histories from an undamaged structure. Coefficients of the AR model are selected as the damage-sensitive features for the subsequent control chart analysis. Finally, the AR coefficients of the models fit to subsequent new data are monitored relative to the control limits. A unique aspect of this study is the coupling of various projection techniques such as principal component analysis, linear and quadratic discriminant operators with the statistical process control in an effort to enhance the discrimination between features from the undamaged and damaged structures. This combined statistical procedure is applied to vibration test data acquired from a concrete bridge column as the column is progressively damaged. The coupled approach captures a clearer distinction between undamaged and damaged vibration responses.

TL;DR: Fueled by an ever-increasing diversity of applications, FE model updating expanded beyond the correlation of modal response to handle frequency response functions, static deflections, and time-domain waveforms.