<p>The seismic risks to a structure or a set of structures in a region are usually determined by generating fragility curves that provide the probability of a building responding in a certain manner for a given level of ground motion intensity. Developing fragility curves, however, is challenging as it involves the computationally expensive task of obtaining the maximum response of the selected structures to a suite of ground motions representing the seismic hazard of the region selected. </p><p>This study presents a methodology to develop surrogate models for the prediction of the maximum responses of buildings to ground motion excitation. Data-driven surrogate models using simple machine learning techniques and physics-based surrogate models using the space mapping technique to map the low-fidelity responses obtained using a multi-degree of freedom shear building model to the high-fidelity values are developed for the prediction of the maximum roof drift ratio and the maximum story drift ratio of a chosen 15-story steel moment-resisting frame building with varying structural properties in California. The predictions of each of these surrogate models are analyzed to assess and compare the performance, capabilities, and limitations of these models. Best practices for developing surrogate models for the prediction of maximum responses of structures to ground motion are recommended.</p><p>The results from the development of data-driven surrogate models show that the spectral displacement is the best intensity measure to condition the maximum roof drift ratio, and the spectral velocity is the best intensity measure to condition the maximum story drift ratio. Fragility analysis of the structure is thus conducted using maximum story drift as the engineering demand parameter and spectral velocity as the intensity measure. Monte Carlo simulation is conducted using the physics-based surrogate model to estimate the maximum story drifts for ground motions that are incrementally scaled to different intensity levels. Maximum likelihood estimates are used to obtain the parameters for a lognormal distribution and the 95% confidence intervals are obtained using the Wald confidence interval to plot the fragility curves.</p><p>Fragility curves are plotted both with and without variations in the structural properties of the building, and it is found that the effects of variability in ground motions on the fragility are far higher than the effects of the randomness of structural properties. Finally, it is found that about 65 ground motion records are needed for convergence of the parameters of the lognormal distribution for plotting fragility curves by using Monte Carlo simulation.</p>

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Origami-based mechanisms have unique characteristics that make them attractive for engineering applications. However, origami-based design is still a developing area of design. Continued work to increase general understanding of key design parameters specific to origami-based mechanisms will increase the ability of designers to capture the potential benefits of origami-based mechanisms. This thesis presents a fundamental study of origami to assist designers in gaining a stronger understanding of the key parameters and capabilities of origami-based mechanisms. As a starting point a study of fundamental motions in action origami models (those that exhibit motions in their folded state) is presented to explore fundamental motions and actuation in origami-based mechanisms. Eleven fundamental motions are outlined and defined with the associated actuation forces that drive them. Additionally, considerations for ensuring necessary performance and force transfer characteristics in origami mechanisms are presented. This is done by exploring the effect of surrogate hinge selections, fold pattern modification, and actuation inputs on the final mechanism. A model of mechanical advantage in origami models consisting of N, degree-4, vertices (where N = 1,2,3,...) is developed and explored. From the exploration of the parameters of the mechanical advantage model it is shown that hinge selection can greatly affect the performance of an origami mechanism by determining its range of motion, precision, and mechanical advantage. Therefore, in order to better understand this important design decision, specific considerations for surrogate hinge selection are presented. These considerations discuss methods to increase performance and reduce hinge imprint, as well as develop surrogate hinges in metals. The key design parameters and considerations presented herein as well as study of origami motions serve to lay the groundwork toward the development of analysis tools and design guidelines specifically suited to origami based design.

Origami-inspired design is an emerging field capable of producing compact and efficient designs. The object of a surrogate fold is to provide a fold-like motion in a non-paper material without undergoing yielding. Compliant mechanisms provide a means to achieve these objectives as large deflections are achieved. The purpose of this thesis is to present a summary of existing compliant joints suitable for use as surrogate folds. In doing so, motions are characterized which no existing compliant joint provides. A series of compliant joints is proposed which provides many of these motions. The possibility of patterning compliant joints to form an array is discussed. Arrays capable of producing interesting motions are noted.

Conducting research experiments on plutonium electrorefining is difficult due to the significant hazards and regulations associated with nuclear materials. Finding a surrogate for plutonium electrorefining studies would enable more fundamental research to be conducted. Potential surrogates were identified by determining the physical properties required to conduct electrorefining using a molten metal and molten salt in CaCl2 at 1123 K. More potential surrogates were identified by changing the matrix salt to be a LiCl-KCl-CaCl2 eutectic salt with electrorefining conducted at 673-773 K. Ce-CeCl3, In-InCl3, Zn-ZnCl2, Sn-SnCl2, and Bi-BiCl¬3 were investigated as potential plutonium electrorefining surrogates. Ce electrorefining in molten CaCl2 resulted in a difficult to separate colloid mixture of Ce, Ca and Cl. Electrorefining rates for In were too slow due to InCl3 volatilizing out of the molten salt. Zn was successfully electrorefined, but the metal obtained did not coalesce into one piece. Sn and Bi were successfully electrorefined and coalesced into solid product rings with high yields and coulombic efficiencies. While a surrogate could not be identified using the same conditions as plutonium electrorefining, two possible surrogates, Sn-SnCl2 and Bi-BiCl3,¬ were found that could imitate the physical configuration (i.e., molten salt on top of molten metal) of plutonium electrorefining at a reduced temperature using a eutectic LiCl-KCl-CaCl2 salt in place of CaCl2. Using this surrogate enables fundamental studies of aspects of plutonium electrorefining. One aspect of plutonium electrorefining research is to improve its efficiency and yield. Plutonium electrorefining is a time-intensive process which generates radioactive waste. Improvements in efficiency and yield can reduce process time and waste. One possible way of improving the efficiency of plutonium electrorefining is to study the impact of using an AC superimposed DC waveform. Four AC superimposed DC and two DC electrorefining runs were performed using bismuth as a plutonium surrogate. All six runs showed a high level of yield and coulombic efficiency. All six cathode rings were confirmed to be high-purity bismuth using scanning electron microscopy with energy dispersive x-ray analysis (SEM-EDS). While the results were inconclusive about the ability of AC superimposed DC waveforms to increase the efficiency of bismuth electrorefining, applying an AC superimposed DC waveform did not appear to decrease the efficiency or yield of the process. The change in waveform also did not result in impurities being present in the product cathode ring. Bismuth, in addition to being identified as a viable plutonium surrogate, has been investigated as a potential liquid electrode for molten salt electrorefining. Because of this, its electrochemical properties are of interest. However, bismuth's electrochemical behavior has received scant attention in eutectic LiCl-KCl melts and no studies were found in the ternary LiCl-KCl-CaCl2 melts. LiCl-KCl-CaCl2 melts offer some advantages over eutectic LiCl-KCl, such as lower melting point and higher oxide solubility. Cyclic voltammetry, square wave voltammetry, chronoamperometry, chronopotentiometry and open-circuit chronopotentiometry were used to measure electrochemical parameters, such as diffusivity and standard redox potential of bismuth electrodeposition in LiCl-KCl and LiCl-KCl-CaCl2 eutectics.

A surrogate arm model was created that is capable of being used for oscillometry. This model is capable of being used as a bench top model for blood pressure cuff devices. The arm consists of endplates and internal supports that are 3D printed with ABS, a silicone rubber outer sleeve, and interchangeable arteries made from two silicone rubber strips glued together at the edges. The interchangeable arteries have varying compliances that can be used as different inputs for oscillometric testing. A process was established to measure the artery compliances with a curve fit correlation of 0.95. However, testing revealed that this artery compliance relationship might not be an accurate representation of the artery compliance while it is in the surrogate arm system. A blood pressure cuff was also used with the surrogate arm model to measure changes in artery volume. Testing with the surrogate arm revealed a blood pressure cuff was capable of measuring artery volume changes of 2mL to 8mL consistently within 3.28% error. Volume changes of 1mL were unable to be repeatable measured accurately with a blood pressure cuff.

Designing products and processes is a fundamental aspect of engineering that significantly impacts society and the world. Chemical process design aims to create more efficient and sustainable production processes that consume fewer resources and emit less pollution. Mathematical models that accurately describe process behavior are necessary to make informed and responsible decisions. However, as processes become more complex, purely symbolic formulations may be inadequate, and simulations using tailored computer code become necessary. The decision‐making process in optimal design requires a procedure for choosing the best option while complying with the system’s constraints, for which task optimization approaches are well suited. This doctoral thesis focuses on black‐box optimization problems that arise when using process simulators in optimal process design tasks and assesses the potential of derivative‐free, metaheuristics, and surrogate‐based optimization approaches. The optimal design of natural gas liquefaction processes is the case study of this research. To overcome numerical issues from black‐box problems, the first work of this doctoral thesis consisted of using the globally convergent Nelder‐Mead simplex method to the optimal process design problem. The second work introduced surrogate models to assist the search towards the global optimum of the black‐box problem and an adaptive sampling scheme comprising the optimization of an acquisition function with metaheuristics. Kriging as surrogate models to the simulation‐optimization problems are computationally cheaper and effective predictors suitable for global search. The third work aims to overcome the limitations of acquisition function optimization and the use of metaheuristics. The proposed comprehensive mathematical notation of the surrogate optimization problem was readily implementable in algebraic modeling language software. The presented framework includes kriging models of the objective and constraint functions, an adaptive sampling procedure, a heuristic for stopping criteria, and a readily solvable surrogate optimization problem with mathematical programming. The success of the surrogate‐based optimization framework relies on the kriging models’ prediction accuracy regarding the underlying, simulation‐based functions. The fourth publication extends the previous work to multi‐objective black‐box optimization problems. It applies the ε constraint method to transform the multi‐objective surrogate optimization problem into a sequence of single‐objective ones. The ε‐constrained surrogate optimization problems are implemented automatically in algebraic modeling language software and solved using a gradient‐based, state‐of‐the‐art solver. The fifth publication is application-driven and focuses on identifying the most suitable mixed‐refrigerant refrigeration technology for natural gas liquefaction in terms of energy consumption and costs. The study investigates five natural gas liquefaction processes using particle swarm optimization and concludes that there are flaws in the expected relationships between process complexity, energy consumption, and total annualized costs. In conclusion, the research conducted in this doctoral thesis demonstrates the importance and capabilities of using optimization to process simulators. The work presented here highlights the potential of surrogate‐based optimization approaches to significantly reduce the computational cost and guide the search in black‐box optimization problems with chemical process simulators embedded. Overall, this doctoral thesis contributes to developing optimization strategies for complex chemical processes that are essential for addressing some of the current most pressing environmental and social challenges. The methods and insights presented in this work can help engineers and scientists design more sustainable and efficient processes, contributing to a better future for all.

Traffic conflicts associated to signalized intersections are one of the major contributing factors to crash occurrences. Driver behavior plays an important role in the safety concerns related to signalized intersections. In this research effort, dynamics of driver behavior in relation to the traffic conflicts occurring at the onset of yellow is investigated. The area ahead of intersections in which drivers encounter a dilemma to pass through or stop when the yellow light commences is called Dilemma Zone (DZ). Several DZ-protection algorithms and advance signal settings have been developed to accommodate the DZ-related safety concerns. The focus of this study is on drivers' decision dynamics, human learning, and choice behavior in DZ, and DZ-related safety measures. First, influential factors to drivers' decision in DZ were determined using a driver behavior survey. This information was applied to design an adaptive experiment in a driving simulator study. Scenarios in the experimental design are aimed at capturing drivers learning process while experiencing safe and unsafe signal settings. The result of the experiment revealed that drivers do learn from some of their experience. However, this learning process led into a higher level of risk aversion behavior. Therefore, DZ-protection algorithms, independent of their approach, should not have any concerns regarding drivers learning effect on their protection procedure. Next, the possibility of predicting drivers' decision in different time frames using different datasets was examined. The results showed a promising prediction model if the data collection period is assumed 3 seconds after yellow. The prediction model serves advance signal protection algorithms to make more intelligent decisions. In the next step, a novel Surrogate Safety Number (SSN) was introduced based on the concept of time to collision. This measure is applicable to evaluate different DZ-protection algorithms regardless of their embedded methodology, and it has the potential to be used in developing new DZ-protection algorithms. Last, an agent-based human learning model was developed integrating machine learning and human learning techniques. An abstracted model of human memory and cognitive structure was used to model agent's behavior and learning. The model was applied to DZ decision making process, and agents were trained using the driver simulator data. The human learning model resulted in lower and faster-merging errors in mimicking drivers' behavior comparing to a pure machine learning technique. / Ph. D.

This research details a procedure for the systematic design of custom foot orthotics based on simulation models and their validation through experimental and clinical studies. These models may ultimately be able to replace the use of empirical tables for designing custom foot orthotics and enable optimal design thicknesses based on the body weight and activities of end-users. Similarly, they may facilitate effortless simulation of various orthotic and loading conditions, changes in material properties, and foot deformities by simply altering model parameters. Finally, these models and the corresponding results may also form the basis for subsequent design of a new generation of custom foot orthotics. Two studies were carried out, the first involving a methodical approach to development of engineering analysis models using the FEA technique. Subsequently, for model verification and validation purposes, detailed investigations were executed through experimental and clinical studies. The results were within 15% difference for the experimental studies and 26% for the clinical studies, and most of the probability values were greater than α= 0.05 accepting our null hypothesis that the FEA model data versus clinical trial data are not significantly different. The accuracy of the FEA model was further enhanced when the uniform loading condition was replaced with a more realistic pressure distribution of 70% of the weight in the heel and the rest in the front portion of the orthotic. This alteration brought the values down to within 22% difference of the clinical studies, with the P-values once again showed no significant difference between the modified FEA model and the clinical studies for most of the scenarios. The second study dealt with the development of surrogate models from FEA results, which can then be used in lieu of the computationally intensive FEA-based analysis models in the engineering design of CFO. Four techniques were studied, including the second-order polynomial response surface, Kriging, non-parametric regression and neural networking. All four techniques were found to be computationally efficient with an average of over 200% savings in time, and the Kriging technique was found to be the most accurate with an average % difference of below 0.30 for each of the loading conditions (light, medium and heavy). The two studies clearly indicate that engineering modeling, analysis and design using FEA techniques coupled with surrogate modeling methods offer a consistent, accurate and reliable alternative to empirical clinical studies. This powerful alternative simulation-based design framework can be a viable and valuable tool in the custom design of orthotics based on an individual's unique needs and foot characteristics. With these capabilities, the CFO prescriber would be able to design and develop the best-fit CFO with the optimal design characteristics for each individual customer without relying upon extensive and expensive trial and error ad hoc approaches. Such a model could also facilitate the inspection of robustness of resulting designs, as well as enable visual inspection of the impact of even small changes on the overall performance of the CFO. By adding the results from these studies to the CFO community, the prescription process may become more efficient and therefore more affordable and accessible to all populations and groups.