Ph.D. Thesis Defense Announcement

INPUT-STATE ESTIMATION OF INELASTIC STRUCTURAL SYSTEMS: THEORETICAL FRAMEWORK AND EXPERIMENTAL VALIDATION

By Nadine Fahed

Advisors:

Dr. Lauren Stewart (CEE) & Dr. Yang Wang (CEE)

Committee Members: 

Dr. Ryan Sherman (CEE)

Dr. Danny Smyl (CEE)

Dr. Gilbert Hegemier (UCSD)

Date and Time:  November 21, 2024.  12:00 pm EST (9:00 am PT)

Location: Price Gilbert 4222

Virtual: https://gatech.zoom.us/j/95533168584

System identification through online estimation algorithms allows for a
comprehensive understanding, prediction, and assessment of the intricate
behaviors exhibited by complex in-situ systems in a variety of applications. This
model-based technique leverages the system's noisy output data and integrates
existing mathematical physics-based models to infer the unknown inputs or
unobservable dynamic states. The adoption of these online techniques in real-world
settings has gained momentum over the past several years, owing to their ease of
implementation, the robustness and reliability of the results, and the continuous
advancements in sensing technologies. Furthermore, when structural systems are loaded beyond their elastic limit, they exhibit inelastic behavior which necessitate
different methods to accommodate this complex nonlinear phenomenon. This
dissertation contributes to this research area by developing a robust framework that
integrates hysteretic models with nonlinear stochastic filtering methods to quantify
the input characteristics of systems exhibiting inelastic behavior due to material
plasticity.
Towards this goal, an input-state estimator for linear systems is first
established. The estimator is designed to reduce the dependency on heuristically
chosen input statistics by incorporating an online input covariance updating routine.
Numerical and experimental validation is conducted, the results of which highlight
the robustness of the estimator in successfully tracking the input and state time
history for various initialized input statistics. The estimator is then extended to
nonlinear systems using an Extended Kalman framework. To efficiently model the
system dynamics in the presence of hysteresis or plastic deformation, two modeling
approaches of the continuum system are explored: an equivalent single degree of
freedom formulation combined with a uniaxial Bouc-Wen model and a planar
multiaxial hysteretic beam model. A comprehensive numerical validation of the
proposed framework is conducted to gain insight into the performance of the
inelastic models and the estimation algorithm. The results underscored the
effectiveness of the proposed estimator and integrated models in successfully
characterizing the input and states in the presence of nonlinearities in the system.
Finally, the framework is experimentally validated using data collected from a beam
subjected to an impact at its midspan. The novel estimator, along with the integrated
models, adequately tracked the impulsive load. As such, these efforts represent an
important contribution to the experimental validation of joint input-state estimation
methods for inelastic continuum structures subjected to high-rate dynamic inputs.