A bayesian probabilistic approach to structural health monitoring

A Bayesian probabilistic methodology for on-line structural health monitoring which addresses the issue of parameter uncertainty inherent in problem is presented. The method uses modal parameters for a limited number of modes identified from measurements taken at a restricted number of degrees of freedom of a structure as the measured structural data. The application presented uses a linear structural model whose stiffness matrix is parameterized to develop a class of possible models. Within the Bayesian framework, a joint probability density function (PDF) for the model stiffness parameters given the measured modal data is determined. Using this PDF, the marginal PDF of the stiffness parameter for each substructure given the data can be calculated.

Monitoring the health of a structure using these marginal PDFs involves two steps. First, the marginal PDF for each model parameter given modal data from the undamaged structure is found. The structure is then periodically monitored and updated marginal PDFs are determined. A measure of the difference between the calibrated and current marginal PDFs is used as a means to characterize the health of the structure. A procedure for interpreting the measure for use by an expert system in on-line monitoring is also introduced.

The probabilistic framework is developed in order to address the model parameter uncertainty issue inherent in the health monitoring problem. To illustrate this issue, consider a very simplified deterministic structural health monitoring method. In such an approach, the model parameters which minimize an error measure between the measured and model modal values would be used as the "best" model of the structure. Changes between the model parameters identified using modal data from the undamaged structure and subsequent modal data would be used to find the existence, location and degree of damage. Due to measurement noise, limited modal information, and model error, the "best" model parameters might vary from one modal dataset to the next without any damage present in the structure. Thus, difficulties would arise in separating normal variations in the identified model parameters based on limitations of the identification method and variations due to true change in the structure. The Bayesian framework described in this work provides a means to handle this parametric uncertainty.

The probabilistic health monitoring method is applied to simulated data and laboratory data. The results of these tests are presented.

Transition metal clusters supported by multinucleating ligand frameworks as models of biological active sites

This dissertation describes efforts to model biological active sites with small molecule clusters. The approach used took advantage of a multinucleating ligand to control the structure and nuclearity of the product complexes, allowing the study of many different homo- and heterometallic clusters. Chapter 2 describes the synthesis of the multinucleating hexapyridyl trialkoxy ligand used throughout this thesis and the synthesis of trinuclear first row transition metal complexes supported by this framework, with an emphasis on tricopper systems as models of biological multicopper oxidases. The magnetic susceptibility of these complexes were studied, and a linear relation was found between the Cu-O(alkoxide)-Cu angles and the antiferromagnetic coupling between copper centers. The triiron(II) and trizinc(II) complexes of the ligand were also isolated and structurally characterized.

Chapter 3 describes the synthesis of a series of heterometallic tetranuclear manganese dioxido complexes with various incorporated apical redox-inactive metal cations (M = Na⁺, Ca²⁺, Sr²⁺, Zn²⁺, Y³⁺). Chapter 4 presents the synthesis of heterometallic trimanganese(IV) tetraoxido complexes structurally related to the CaMn₃ subsite of the oxygen-evolving complex (OEC) of Photosystem II. The reduction potentials of these complexes were studied, and it was found that each isostructural series displays a linear correlation between the reduction potentials and the Lewis acidities of the incorporated redox-inactive metals. The slopes of the plotted lines for both the dioxido and tetraoxido clusters are the same, suggesting a more general relationship between the electrochemical potentials of heterometallic manganese oxido clusters and their “spectator” cations. Additionally, these studies suggest that Ca²⁺ plays a role in modulating the redox potential of the OEC for water oxidation.

Chapter 5 presents studies of the effects of the redox-inactive metals on the reactivities of the heterometallic manganese complexes discussed in Chapters 3 and 4. Oxygen atom transfer from the clusters to phosphines is studied; although the reactivity is kinetically controlled in the tetraoxido clusters, the dioxido clusters with more Lewis acidic metal ions (Y³⁺ vs. Ca²⁺) appear to be more reactive. Investigations of hydrogen atom transfer and electron transfer rates are also discussed.

Appendix A describes the synthesis, and metallation reactions of a new dinucleating bis(N-heterocyclic carbene)ligand framework. Dicopper(I) and dicobalt(II) complexes of this ligand were prepared and structurally characterized. A dinickel(I) dichloride complex was synthesized, reduced, and found to activate carbon dioxide. Appendix B describes preliminary efforts to desymmetrize the manganese oxido clusters via functionalization of the basal multinucleating ligand used in the preceding sections of this dissertation. Finally, Appendix C presents some partially characterized side products and unexpected structures that were isolated throughout the course of these studies.

Expanding the Toolkit for Synthetic Biology: Frameworks for Native-like Non-natural Gene Circuits

Synthetic biology combines biological parts from different sources in order to engineer non-native, functional systems. While there is a lot of potential for synthetic biology to revolutionize processes, such as the production of pharmaceuticals, engineering synthetic systems has been challenging. It is oftentimes necessary to explore a large design space to balance the levels of interacting components in the circuit. There are also times where it is desirable to incorporate enzymes that have non-biological functions into a synthetic circuit. Tuning the levels of different components, however, is often restricted to a fixed operating point, and this makes synthetic systems sensitive to changes in the environment. Natural systems are able to respond dynamically to a changing environment by obtaining information relevant to the function of the circuit. This work addresses these problems by establishing frameworks and mechanisms that allow synthetic circuits to communicate with the environment, maintain fixed ratios between components, and potentially add new parts that are outside the realm of current biological function. These frameworks provide a way for synthetic circuits to behave more like natural circuits by enabling a dynamic response, and provide a systematic and rational way to search design space to an experimentally tractable size where likely solutions exist. We hope that the contributions described below will aid in allowing synthetic biology to realize its potential.

Real-Time Bayesian Analysis of Ground Motion Envelopes for Earthquake Early Warning

Current earthquake early warning systems usually make magnitude and location predictions and send out a warning to the users based on those predictions. We describe an algorithm that assesses the validity of the predictions in real-time. Our algorithm monitors the envelopes of horizontal and vertical acceleration, velocity, and displacement. We compare the observed envelopes with the ones predicted by Cua & Heaton's envelope ground motion prediction equations (Cua 2005). We define a "test function" as the logarithm of the ratio between observed and predicted envelopes at every second in real-time. Once the envelopes deviate beyond an acceptable threshold, we declare a misfit. Kurtosis and skewness of a time evolving test function are used to rapidly identify a misfit. Real-time kurtosis and skewness calculations are also inputs to both probabilistic (Logistic Regression and Bayesian Logistic Regression) and nonprobabilistic (Least Squares and Linear Discriminant Analysis) models that ultimately decide if there is an unacceptable level of misfit. This algorithm is designed to work at a wide range of amplitude scales. When tested with synthetic and actual seismic signals from past events, it works for both small and large events.