published_or_final_version / Civil and Structural Engineering / Master / Master of Philosophy

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

Most existing computational methods for inverting material properties of multilayer systems have focused primarily on elastic properties of materials or a static approach. Typically, they are based on a two-stage approach: (I) modeling structural responses with a computer program, and (II) estimating layer properties mathematically using the response outputs determined in stage I without interactions with the governing state partial-differential-equation (PDE) of stage I. This two-stage approach may not be accurate and efficient enough for inverting larger scale model parameters. The objective of this research was to develop a computational method to invert dynamic moduli of multilayer systems with applications to flexible pavements under falling weight deflectometer (FWD) tests, thereby advancing existing methods and fostering understanding of material behaviors. This research first developed a finite-element and Newton-Raphson method to invert layer elastic moduli using FWD data. The model improved the moduli seeds estimation and achieved a satisfactory accuracy based on Monte Carlo simulations, addressing the common back-calculation issue of no unique solutions. Consequently, a time-domain finite-element method was developed to simulate dynamic-viscoelastic responses of the multilayer systems under loading pulses. Simulation results demonstrated that the dynamic-viscoelastic-damping-coupled model could emulate structural responses more accurately, thereby advancing existing simulation approaches. By using the dynamic-viscoelastic-response model as one computation module, this research led to the development of a PDE-constrained Lagrangian optimization method to invert dynamic moduli and viscoelastic properties of multilayer systems. The Lagrangian function was used as an objective function, with a regularization term and governing-state PDE constraint. Both the first-order (gradient) and second-order variation (Hessian matrix) of the Lagrangian were computed to satisfy necessary and sufficient optimality conditions, and Armijo rule was modified to determine a stable step length. The developed method improved computation speed significantly, and it is superior for large-scale inverse problems. The model was implemented for evaluating flexible pavements under FWD tests and for inverting the master curve of dynamic moduli of the asphalt layer. Independent computer coding was developed for all numerical methods. The computational methods developed may also be applied to other multilayer systems, such as tissues and sandwich structures at different time and length scales. / text

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A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / Introduction: Patients who develop delirium while hospitalized are increasingly recognized as at risk for the development of long term cognitive impairment. We became interested in the contribution of delirium to the development of post-operative cognitive dysfunction (POCD) when we found that patients at Mayo Clinic in Arizona, compared to patients at the Mayo facilities in Rochester, MN, were 17 times more likely to receive the drug physostigmine (Antilirium®) for the treatment of delirium in the Post Anesthesia Care Unit (PACU). However, before we could examine the relationship between delirium and POCD we needed to validate a tool we could use to quickly assess the presence of delirium in patients emerging from anesthesia in the PACU. Hypothesis: The Confusion Assessment Method in the Intensive Care Unit (CAM-ICU) can be used in the PACU to identify patients with delirium. Methods: Patients 65 years of age or greater who were going to have a standardized general anesthetic for a surgical procedure were identified on the day of surgery and consent to participate in the study was obtained. The CAM-ICU was used preoperatively to determine study eligibility (patients who scored less than 7 [scale of 1-10], indicating delirium, on the test were not followed further) and postoperatively, one hour after the patient was admitted to the PACU, to assess for delirium. The CAM-ICU was administered after we asked the patient’s nurse whether or not he or she had determined that the patient was delirious. Results: 168 patients, mean age 75 ± 7 (SD) with the majority of participants having urologic or orthopedic procedures were assessed pre- and post-operatively with the CAM-ICU, and post-operatively by a nursing assessment for delirium. The CAM-ICU took little time to administer and was easy for patients to understand and use. The nurse at the bedside identified 5 of 168 patients as delirious (prevalence of 2.98%). The CAM-ICU was positive for delirium in 11 of 168 (6.55%). The CAM-ICU had a sensitivity of 60% (3/5) and a specificity of 95% (155/163). Conclusion: In this investigation, the CAM-ICU was easy to use and had a high specificity for identifying post-operative delirium.

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