This thesis seeks to explore the architectural transformation of residential space for changes and adjustments as we find our lifestyles altering. With the understanding that change is often unpredictable, we must be prepared for adaptations to new and revised living environments. Change appears in many ways: marriage or cohabitation, having children, empty nesting, aging, caring for elder family members, illness, and death. Too often we design our homes for the present, with little thought of future needs. Universal and adjustable design must become an everyday part of an architectʼs repertoire when embarking on new projects with their clients. Even architects, working on “cookie cutter” projects, must bring a more sustainable approach to their designs. Taking a closer look on said "cookie cutter” projects, adjustable design must start from a broader spectrum, beyond the site, focusing on the development as a whole and its connection with its infrastructure. Within a community, camaraderie and conversation are major factors in the success of a residential development. The central focus of this paper will be the architectural adjustability of the home.

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Housing shortages have plagued many large North American cities and urban areas over the last several decades. In many such regions, less affluent areas are rapidly redeveloped and densified to keep up with housing demand. This frenetic development displaces lower income residents and tears apart community networks. Meanwhile, affluent areas resist development, maintaining low densities despite their relative proximities to jobs, schools, transportation networks, and other resources. Consequently, patterns of inequality which have persisted in American Cities for decades, if not centuries, remain in-tact. Furthermore, these low-density areas contribute to sprawl, car culture, habitat destruction, and other harmful social and environmental phenomena. Additionally, many of the low density urban and suburban residential neighborhoods which were developed en masse over the last century–so-called 'cookie-cutter' neighborhoods–fail to readily accommodate the diverse and ever-changing needs and circumstances of the people who currently inhabit them, having been built with outdated and inflexible notions of the 20th century ideal family in mind. This thesis explores the redevelopment of a single family residential neighborhood in Washington, D.C. By exploring the densification of the neighborhood and the addition of new programs to the suburban landscape, the thesis seeks to identify strategies by which we might one day convert massive and sprawling cookie-cutter suburbs into denser, more sustainable, and more diverse neighborhoods which serve a wider array of residents better while contributing additional housing and other resources to the broader population. / Master of Architecture / The American obsession with single-family homeownership in the name of the 'American Dream' has led to the development of an unsustainable landscape characterized by the extreme stratification of land uses, widespread overdependence on the personal vehicle, and the continued issue of equal access to community assets and services, among many other issues. Furthermore, many extant suburban landscapes were designed with outdated and inflexible notions of the ideal family in mind, and thus they fail to meet the needs of families and individuals who don't conform to the typical family model of the 20th century. The thesis takes the stance that the 'American Dream' is an outdated ideal, and that the American suburb is, by extension, an outdated model of living in the 21st century. The thesis investigates the reprogramming of an affluent single family residential neighborhood in Washington, D.C, proposing the densification of the housing stock and exploring new urban forms which aim to build density, diversity, sustainability, and community in an existing suburban-type neighborhood.

<div>This dissertation addresses two significant issues in the effective application of machine learning algorithms and models for the physical and engineering sciences. The first is the broad challenge of automated modeling of data across different processes in a physical system. The second is the dilemma of obtaining insightful interpretations on the relationships between the inputs and outcome of a system as inferred from complex, black box machine learning models.</div><div><br></div><div><b>Automated Geometric Shape Deviation Modeling for Additive Manufacturing Systems</b></div><div><b><br></b></div><div>Additive manufacturing systems possess an intrinsic capability for one-of-a-kind manufacturing of a vast variety of shapes across a wide spectrum of processes. One major issue in AM systems is geometric accuracy control for the inevitable shape deviations that arise in AM processes. Current effective approaches for shape deviation control in AM involve the specification of statistical or machine learning deviation models for additively manufactured products. However, this task is challenging due to the constraints on the number of test shapes that can be manufactured in practice, and limitations on user efforts that can be devoted for learning deviation models across different shape classes and processes in an AM system. We develop an automated, Bayesian neural network methodology for comprehensive shape deviation modeling in an AM system. A fundamental innovation in this machine learning method is our new and connectable neural network structures that facilitate the transfer of prior knowledge and models on deviations across different shape classes and AM processes. Several case studies on in-plane and out-of-plane deviations, regular and free-form shapes, and different settings of lurking variables serve to validate the power and broad scope of our methodology, and its potential to advance high-quality manufacturing in an AM system.</div><div><br></div><div><b>Interpretable Machine Learning</b></div><div><b><br></b></div><div>Machine learning algorithms and models constitute the dominant set of predictive methods for a wide range of complex, real-world processes. However, interpreting what such methods effectively infer from data is difficult in general. This is because their typical black box natures possess a limited ability to directly yield insights on the underlying relationships between inputs and the outcome for a process. We develop methodologies based on new predictive comparison estimands that effectively enable one to ``mine’’ machine learning models, in the sense of (a) interpreting their inferred associations between inputs and/or functional forms of inputs with the outcome, (b) identifying the inputs that they effectively consider relevant, and (c) interpreting the inferred conditional and two-way associations of the inputs with the outcome. We establish Fisher consistent estimators, and their corresponding standard errors, for our new estimands under a condition on the inputs' distributions. The significance of our predictive comparison methodology is demonstrated with a wide range of simulation and case studies that involve Bayesian additive regression trees, neural networks, and support vector machines. Our extended study of interpretable machine learning for AM systems demonstrates how our method can contribute to smarter advanced manufacturing systems, especially as current machine learning methods for AM are lacking in their ability to yield meaningful engineering knowledge on AM processes. <br></div>