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Early design is the most critical stage to improve the resiliency and sustainability of buildings. An unaided early design follows the designer's accustomed domain of knowledge and cognitive biases. Given the inherent limitations of human decision-making, such a design process will only explore a small set of alternatives using limited criteria, and most likely, miss high-performing alternatives. Performance-based engineering (PBE) is a probabilistic approach to quantify buildings performance against natural hazards in terms of decision metrics such as repair cost and functionality loss. Therefore, PBE can remarkably improve early design by informing the designer regarding the possible consequences of different decisions. Incorporating PBE in early design is obstructed by several challenges such as time- and effort-intensiveness of performing rigorous PBE assessments, a specific skillset that might not be available, and accrual of aleatoric (associated with innate randomness of physical systems properties and surrounding environment conditions) and epistemic (associated with the incomplete state of knowledge) uncertainties. In addition, a successful early design requires exploring a large number of alternatives, which, when compounded by PBE assessments, will significantly exhaust computational resources and pressure the project timeline. This dissertation proposes a framework to integrate prior knowledge and PBE assessments in early design. The primary workflow in the proposed framework develops a performance inventory to train statistical surrogate models using supervised learning algorithms. This performance inventory comprises PBE assessments consistent with building taxonomy and site, and is supported by a knowledge-based module. The knowledge-based module organizes prior published PBE assessments as a relational database to supplement the performance inventory and aid early design exploration through knowledge-based surrogate models. Lastly, the developed knowledge-based and data-driven surrogate models are implemented in a sequential design exploration scheme to estimate the performance range for a given topology and building system. The proposed framework is then applied for mid-rise concrete office buildings in Charleston, South Carolina, where seismic vulnerability and environmental performance are linked to topology and design parameters. / Doctor of Philosophy / Recent advances in structural engineering aspire to achieve higher societal objectives than focusing solely on safety. Two main current objectives are resiliency (i.e., the built environment's ability to rapidly and equitably recover after an external shock, among other definitions) and sustainability (i.e., the ability to meet current needs without preventing future generations from meeting theirs, among other definitions). Therefore, holistic design approaches are needed that can include and explicitly evaluate these objectives at different steps, particularly the earlier stages. The importance of earlier stages stems from the higher freedom to make critical decisions – such as material and building system selection – without incurring higher costs and effort on the designer. Performance-based engineering (PBE) is a quantitative approach to calculating the impact of natural hazards on the built environment. The calculated impacts from PBE can then be communicated through a more easily understood language such as monetary values. However, several challenges should be first addressed to apply PBE in early design. First, PBE assessments are time- and effort-intensive and require expertise that might not be available to the designer. Second, a typical early design exploration evaluates many alternatives, significantly increasing the already high computational and time cost. Third, PBE requires detailed design and building information which is not available at the preliminary stages. This lack of knowledge is coupled with additional uncertainties due to the random nature of natural hazards and building system characteristics (e.g., material strength or other mechanical properties). This dissertation proposes a framework to incorporate PBE in early design, and tests it for concrete mid-rise offices in Charleston, South Carolina. The centerpiece of this framework is to use data-driven modeling to learn directly from assessments. The data-driven modeling treats PBE as a pre-configured data inventory and develops statistical surrogate models (i.e., simplified mathematical models). These models can then relate early design parameters to building seismic and environmental performance. The inventory is also supported by prior knowledge, structured as a database of published literature on PBE assessments. Lastly, the knowledge-based and data-driven models are applied in a specific order to narrow the performance range for given building layout and system.

Artificial intelligence (AI) has progressed significantly during the last several decades, along with the rapid advancements in computational power. This advanced technology is currently being employed in various engineering fields, not just in computer science. In aerospace engineering, AI and machine learning (ML), a major branch of AI, are now playing an important role in various applications, such as automated systems, unmanned aerial vehicles, aerospace optimum design structure, etc. This dissertation mainly focuses on structural engineering to employ AI to develop lighter and safer aircraft structures as well as challenges involving structural optimization and analysis. Therefore, various ML applications are studied in this research to provide novel frameworks for structural optimization, analysis, and design. First, the application of a deep-learning-based (DL) convolutional neural network (CNN) was studied to develop a surrogate model for providing optimum structural topology. Typically, conventional structural topology optimization requires a large number of computations due to the iterative finite element analyses (FEAs) needed to obtain optimal structural layouts under given load and boundary conditions. A proposed surrogate model in this study predicts the material density layout inputting the static analysis results using the initial geometry but without performing iterative FEAs. The developed surrogate models were validated with various example cases. Using the proposed method, the total calculation time was reduced by 98 % as compared to conventional topology optimization once the CNN had been trained. The predicted results have equal structural performance levels compared to the optimum structures derived by conventional topology optimization considered ``ground truths". Secondly, reinforcement learning (RL) is studied to create a stand-alone AI system that can design the structure from trial-and-error experiences. RL application is one of the major ML branches that mimic human behavior, specifically how human beings solve problems based on their experience. The main RL algorithm assumes that the human problem-solving process can be improved by earning positive and negative rewards from good and bad experiences, respectively. Therefore, this algorithm can be applied to solve structural design problems whereby engineers can improve the structural design by finding the weaknesses and enhancing them using a trial and error approach. To prove this concept, an AI system with the RL algorithm was implemented to drive the optimum truss structure using continuous and discrete cross-section choices under a set of given constraints. This study also proposed a unique reward function system to examine the constraints in structural design problems. As a result, the independent AI system can be developed from the experience-based training process, and this system can design the structure by itself without significant human intervention. Finally, this dissertation proposes an ML-based classification tool to categorize the vibrational mode shapes of tires. In general, tire vibration significantly affects driving quality, such as stability, ride comfort, noise performance, etc. Therefore, a comprehensive study for identifying the vibrational features is necessary to design the high-performance tire by considering the geometry, material, and operation conditions. Typically, the vibrational characteristics can be obtained from the modal test or numerical analysis. These identified modal characteristics can be used to categorize the tire mode shapes to determine the specific mode cause poorer driving performances. This study suggests a method to develop an ML-based classification tool that can efficiently categorize the mode shape using advanced feature recognition and classification algorithms. The best-performed classification tool can accurately predict the tire category without manual effort. Therefore, the proposed classification tool can be used to categorize the tire mode shapes for subsequent tire performance and improve the design process by reducing the time and resources for expensive calculations or experiments. / Doctor of Philosophy / Artificial intelligence (AI) has significantly progressed during the last several decades with the rapid advancement of computational capabilities. This advanced technology is currently employed to problems in various engineering fields, not just problems in computer science. Machine learning (ML), a major branch of AI, is actively applied to mechanical/structural problems since an ML model can replace a physical system with a surrogate model, which can be used to predict, control, and optimize its behavior. This dissertation provides a new framework to design and analyze structures using ML-based techniques. In particular, the latest ML technologies, such as convolutional neural networks, widely used for image processing and feature recognition, are applied to replace numerical calculations in structural optimization and analysis with the ML-based system. Also, this dissertation suggests how to develop a smart system that can design the structure by itself using reinforcement learning, which is utilized for autonomous driving systems and robot walking algorithms. Finally, this dissertation suggests an ML-based classification approach to categorize complex vibration modes of a structure.

Salmonella contamination of spices and other low water activity foods is a growing concern for the food industry due to increased frequency of salmonellosis outbreaks and detection-based product recalls. The impact of inoculation preparation on the survival of a Salmonella enterica and its proposed surrogate, Enterococcus faecium NRRL B-2385, on the whole black peppercorns and cumin seeds was examined. Three liquid inoculation methods (biofilm-inclusion, agar-grown, broth-grown) for Salmonella enterica and surrogate Enterococcus faecium and one dry transfer method for Salmonella enterica were developed then applied to whole peppercorn and cumin seeds. Spices were returned to original water activity (aw 0.3) and stored for 28 days with periodic sampling (0, 1, 7, 14, 21, 28 days) and surviving bacteria enumerated. Average log reductions (LR) over time were statistically analyzed to determine differences in stability during storage. Inoculation preparation was associated with significant differences in recovered Salmonella and Enterococcus from both peppercorn and cumin over the storage period. At 28 days, the most stable inoculations of Salmonella resulted from the biofilm-inclusion (-0.04 CFU/g LR) and agar grown (-0.75 CFU/g LR) methods on peppercorn and the biofilm inclusion method (-0.28 CFU/g LR) on cumin. Log reductions of Enterococcus faecium (-0.02 CFU/g LR biofilm-inclusion-peppercorn, -0.19 CFU/g LR agar-grown-peppercorn, -0.61 CFU/g LR biofilm-inclusion-cumin) were comparable to Salmonella after 28d desiccated storage. These results will guide the inoculation strategies for validating inactivation processes for reducing Salmonella on whole spices, and for comparisons of inactivation of Salmonella and its proposed surrogate Enterococcus faecium. / Master of Science in Life Sciences

Exploring the design space of tensegrity systems is the basis of the work presented in this thesis. The areas explored as part of this research include the optimization of tensegrity structures to minimize the size of a tensegrity structure given payload shock constraints, and the control and locomotion of an icosahedral tensegrity system using movable masses and using an accelerometer in conjunction with leveraging geometrical knowledge of an icosahedral tensegrity system to localize the system after the system moves. In the optimization design space, a simplified model was created to represent an icosahedral tensegrity structure. This was done by assuming that a system of springs could represent an icosahedral system with enough fidelity to be useful for optimization. These results were then validated and tested. The most extensive part of the research preformed was in regards to the control of a Tensegrity Icosahedron. This structure utilized novel locomotion techniques to allow the structure to move by changing its center of mass. Essentially, instead of actuating the system by changing the length of the strings that make up the system state, the system's center of mass is moved using movable masses. These masses make it so the system can rotate about one of the base pivot points. A controller was also created that allows for this system to go to a target point if the state of the system is known. Finally, work was done to attempt to localize a structure by combining a motion model based off the geometry of the structure and a measurement model based on accelerometer readings during the movement of the structure into an EKF. This EKF was then used to localize the structure based on the predicted motion model and the measurement model prescribed by the accelerometer. This allowed for the system's state to be estimated to within 3 standard deviations of the uncertainty of the motion and measurement models. Additional work on this system was also done to make a physical model of the system. This work includes making a bar so that movable masses can pass through it, creating an accelerometer model to roughly determine the system's state, and tracking the system’s displacement using some steady-state model assumptions.

This thesis demonstrates a parameter estimation technique for bioprocesses that utilizes measurement noise in experimental data to determine credible intervals on parameter estimates, with this information of potential use in prediction, robust control, and optimization. To determine these estimates, the work implements Bayesian inference using nested sampling, presenting an approach to develop neural network (NN) based surrogate models. To address challenges associated with non-uniform sampling of experimental measurements, an NN structure is proposed. The resultant surrogate model is utilized within a Nested Sampling Algorithm that samples possible parameter values from the parameter space and uses the NN to calculate model output for use in the likelihood function based on the joint probability distribution of the noise of output variables. This method is illustrated against simulated data, then with experimental data from a Sartorius fed-batch bioprocess. Results demonstrate the feasibility of the proposed technique to enable rapid parameter estimation for bioprocesses. / Thesis / Master of Applied Science (MASc) / Bioprocesses require models that can be developed quickly for rapid production of desired pharmaceuticals. Parameter estimation is necessary for these models, especially first principles models. Generating parameter estimates with confidence intervals is important for model based control. Challenges with parameter estimation that must be addressed are the presence of non-uniform sampling and measurement noise in experimental data. This thesis demonstrates a method of parameter estimation that generates parameter estimates with credible intervals by incorporating measurement noise in experimental data, while also employing a dynamic neural network surrogate model that can process non-uniformly sampled data. The proposed technique implements Bayesian inference using nested sampling and was tested against both simulated and real experimental fed-batch data.

Thesis (LLM)--University of Stellenbosch, 2000. / ENGLISH ABSTRACT: The aim of this study is to investigate the possibility of regulating surrogacy in the light of the existing legal position and the relevant moral aspects. It is now medically possible for one woman to bear a child, which is not genetically related to her, on behalf of another person. Currently the law makes no specific provision for the regulation of surrogate motherhood. The key problem in this regard is that the existing law is applicable to surrogate motherhood, inter alia because surrogacy is brought about by artificial insemination and because the intended parents can only acquire parental authority by way of adoption. The final recommendation is a Surrogacy Act for South Africa. The thesis is divided into three parts. The first part is an analysis of the relevant moral and social aspects relating to surrogacy in order to justify the above-mentioned act morally. Commercial surrogacy, the genetic tie between parent and child, the differences between surrogacy and adoption, the question as to who is a parent and surrogacy for convenience are analysed. Despite all the arguments that can be made in favour of or against these moral issues in a vacuum, these arguments will be irrelevant in cases where the child is already born. In such a case the only relevant concern will be what is in the child's best interest. In the second part of the thesis the existing legal position is analysed. This includes an examination of the applicable legislation, the impact of the Constitution, the South African Law Commission's proposed bill on surrogate motherhood and the customary law. Although the relevant legislation does not specifically provide for surrogacy, it remains applicable. This is extremely problematic for the parties involved. The Bill of Rights is applicable to all law and binds the Legislature. Therefore the main principles of the Constitution will have to be embodied in the proposed regulatory Act. Although the Law Commission's proposed bill is a well formulated document, one shortcoming that has been identified is that it is not constitutionally justifiable. The customary law has several practices which are analogous to surrogacy. The right to culture, which is entrenched in the Constitution, has the effect that these practices cannot be outlawed. However, should it not be consistent with the Constitution, it can be held to be invalid. The conclusion which is reached is that surrogacy can be morally and constitutionally justifiable if it is regulated properly. It is therefore proposed that an Act be formulated to regulate these relevant issues. Such a proposed Act is included in part three of the thesis. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die moontlikheid om surrogaatmoederskap te reguleer as gevolg van die feit dat dit nou vir 'n vrou moontlik is om 'n kind, wat nie geneties aan haar verwant is nie, vir 'n ander te baar. Die reg maak nie spesifiek vir die regulering van surrogaatmoederskap voorsiening nie. Die kernprobleem in hierdie verband is dat die bestaande reg wel van toepassing daarop is, vanweë onder andere die feit dat surrogaatmoederskap teweeggebring word deur middel van kunsmatige bevrugting. Wetgewing wat kunsmatige bevrugting reguleer is gevolglik van toepassing, hoewel dit nie geskryf was met die oog op surrogaatmoederskap in die besonder nie. Die doel van die studie is gevolglik om te ondersoek hoe surrogaatmoederskap gereguleer kan word gegewe die bestaande regsposisie en relevante morele oorwegings. Die tesis kan in drie afdelings verdeel word. Die eerste bestaan uit 'n analise van die morele aspekte wat by surrogaatmoederskap ter sprake is. Dit is nodig om hierdie aangeleenthede te analiseer ten einde 'n voorgestelde wet moreel regverdigbaar te maak. Kommersiële surrogaatmoederskap, die genetiese band tussen ouer en kind, die verskille tussen surrogasie en aanneming, die vraag na die identiteit van die ouer en surrogaatmoederskap vir gerief word geanaliseer. Ten spyte van al die morele argumente wat gemaak kan word voordat 'n kind gebore is, is hierdie argumente van weinig belang waar die kind reeds gebore is. In so 'n geval is dit slegs die beste belang van die kind wat oorweeg moet word. Die bestaande regsposisie word in die tweede deel van die tesis ontleed. Dit sluit 'n ontleding van die relevante wetgewing, die oorweging van die impak van die Grondwet, 'n analise van die Suid-Afrikaanse Regskommissie se Voorgestelde Wetsontwerp op Surrogaatmoederskap en 'n evaluering van die inheemse reg in. Die gevolgtrekking wat gemaak word is dat die bestaande wetgewing nie uitdruklik vir surrogaatmoederskap voorsiening maak nie, maar wel daarop van toepassing kan wees. Dit veroorsaak verskeie probleme vir die betrokke partye. Die Grondwet het 'n drastiese impak op die regulering van surrogaatmoederskap en sal in ag geneem moet word indien 'n surrogaatmoederskapswet voorgestel word. Die Regskommissie se voorgestelde wetsontwerp is 'n goed geformuleerde dokument, maar moet aangepas word ten einde grondwetlik regverdigbaar te wees. Daar is verskeie gebruike in die inheemse reg wat analoog aan surrogaatmoederskap is. Die reg op kultuur, wat grondwetlik verskans is, het tot gevolg dat partye, op wie die inheemse reg van toepassing is, die reg het om hierdie gebruike na te volg. Indien die praktyke egter strydig met die Grondwet is, kan dit ongeldig verklaar word. Derdens word 'n wet voorgestelom surrogaatmoederskap te reguleer. Die gevolgtrekking waartoe gekom word, is dat surrogaatmoederskap moreel en grondwetlik regverdigbaar kan wees indien dit behoorlik gereguleer word.

In most randomised controlled trials (RCTs), a large number of patients need to be followed over many years, for the clinical benefit of the drug to be accurately quantified (1). Using an early proxy, or a surrogate endpoint, in place of the direct endpoint of overall survival (OS) could theoretically shorten the duration of RCTs and minimise the exposure of patients to ineffective or toxic treatments (2, 3). This thesis examined the relationship between surrogate endpoints and OS in metastatic colorectal cancer (CRC), advanced non-small cell lung cancer (NSCLC) and metastatic breast cancer (MBC). A review of the literature identified 144 RCTs in metastatic CRC, 189 in advanced NSCLC and 133 in MBC. The publications were generally of poor quality with incomplete reporting on many key variables, making comparisons between studies difficult. The introduction of the CONSORT statement was associated with improvements in the quality of reporting. For CRC (337 arms), NSCLC (429 arms) and MBC (290 arms) there were strong relationships between OS and progression free survival (PFS), time to progression (TTP), disease control rate (DCR), response rate (RR) and partial response (PR). Correlation was also demonstrated between OS and complete response (CR) in CRC and duration of response (DOR) in MBC. However, while strong relationships were found, the proportion of variance explained by the models was small. Prediction bands constructed to determine the surrogate threshold effect size indicated that large improvements in the surrogate endpoints were needed to predict overall survival gains. PFS and TTP showed the most promise as surrogates. The gain in PFS and TTP required to predict a significant gain in overall survival was between 1.2 and 7.0 months and 1.8 and 7.7 months respectively, depending on trial size and tumour type. DCR was a better potential predictor of OS than RR. The results of this study could be used to design future clinical trials with particular reference to the selection of surrogate endpoint and trial size.

An experimental study was performed to study soot formation and evolution in atmospheric, laminar, coflow, diffusion flames of Jet-A1, Synthetic Paraffinic Kerosene and their surrogates. Light extinction, rapid thermocouple insertion and thermophoretic sampling followed by transmission electron microscopy and atomic forced microscopy were used to obtain soot volume fraction profiles, temperature profiles and soot morphologies, respectively. Different soot evolution processes were observed on the flame centerline and on a streamline with a significantly different temperature history. Formation and agglomeration of the first soot particles are different on the two streamlines. Transparent liquid-like particles are produced in large volumes in the early regions of the flame centerline where T < 1500 K; these particles are undetectable by the extinction method with the wavelength of 632.8 nm. Most of the currently used computational soot models do not predict the liquid-like nature of nascent soot particles which has major effects on the modeling.

An experimental study was performed to study soot formation and evolution in atmospheric, laminar, coflow, diffusion flames of Jet-A1, Synthetic Paraffinic Kerosene and their surrogates. Light extinction, rapid thermocouple insertion and thermophoretic sampling followed by transmission electron microscopy and atomic forced microscopy were used to obtain soot volume fraction profiles, temperature profiles and soot morphologies, respectively. Different soot evolution processes were observed on the flame centerline and on a streamline with a significantly different temperature history. Formation and agglomeration of the first soot particles are different on the two streamlines. Transparent liquid-like particles are produced in large volumes in the early regions of the flame centerline where T < 1500 K; these particles are undetectable by the extinction method with the wavelength of 632.8 nm. Most of the currently used computational soot models do not predict the liquid-like nature of nascent soot particles which has major effects on the modeling.