Data-driven Mathematical Modeling

The recent advancements in computational capabilities and sensing technologies provide an excellent opportunity to develop, test, and validate data-driven mathematical models for system identification, condition assessment, and health monitoring of structural systems that may be vibrating in linear and/or nonlinear ranges. In this study, measurements from various large-scale, complex, experimental systems, as well as full-scale real-life multi-input-multi-output (MIMO) structures are used to develop robust mathematical frameworks for response prediction, change detection, nonlinear damping estimation, in addition to displacement-field and operating-load estimation. The systems under consideration are the Yokohama Bay Bridge which was subjected to the 2011 Great East Japan Earthquake; large-scale experimental soil-foundation-superstructure interaction systems subjected to various earthquake excitations with systematically increasing levels of intensity; and a four-story experimental test-bed designed, developed, and fabricated at the University of Southern California. The vibration signatures from these systems are used to assess the viability of existing parametric and nonparametric identification approaches, and to propose new hybrid data-driven computational modeling methods that can accurately capture the correct physics of the underlying complex systems. This presentation is a collection of analytical, computational, and experimental studies that capitalizes on the availability of large datasets to develop tools that can interpret these datasets, and to establish robust frameworks that can extract physically meaningful information, for an informed decision-making.

Language of the event: English

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Armen Derkevorkian is a member of technical staff and a Principal Investigator at the Jet Propulsion Lab at the California Institute of Technology (Caltech). Dr. Derkevorkian has multidisciplinary educational background including two Masters Degrees in Electrical Engineering and Structural Engineering, a Graduate Certificate in Violin Performance, and a PhD in Civil Engineering; all from the University of Southern California (USC), where he was a Viterbi Doctoral Fellow. He is a recent recipient of the JPL Voyager Award for outstanding technical achievement. Derkevorkian’s research interests include data interpretation, data-driven modeling using machine learning, structural health monitoring and condition assessment.

E-mail: [email protected]

Phone:  +374 60 61 26 41