A methodological assessment of extreme heat mortality modeling and heat vulnerability mapping in Atlanta, Detroit, and Phoenix

(Georgia Institute of Technology, 2019-11-12) Mallen, Evan Sheppard

Extreme temperatures pose an increasingly high risk to human health and are projected to worsen in a warming climate with increased intensity, duration and frequency of heat waves, further amplified by the urban heat island, in the coming decades. To mitigate heat exposure and protect sensitive populations, urban planners are increasingly using decision support tools like heat vulnerability indices (HVIs) to identify high priority areas for intervention and investment. However, HVIs often capture only proxy heat exposure indicators at the land surface level, not air temperatures that humans experience, and are highly subjective in their construction methodology. This gap can be filled using regional climate models like the Weather Research & Forecasting (WRF) model to simulate air temperatures comprehensively over a city, coupled with a heat exposure-response function to objectively estimate mortality attributable to heat. But this method is often beyond the capabilities of local planning departments due to limitations in funding or technical expertise to run the model. Careful consideration of decision support tool selection will be an important factor in determining the future resilience of urban populations in a changing climate. Through a comparative analysis, this study investigates the relationship and utility of HVIs and spatial statistical attribution models with a focus on 1) the extent to which HVI methods can replicate spatial prioritization from a WRF-driven mortality model; 2) the relative significance of place-based vulnerabilities used in the HVI; and 3) the potential to reliably replicate a WRF-driven mortality model using publicly available datasets. This information can help urban planners and public health officials improve their emergency response plans and communication strategies for heat mitigation by specifically targeting short and long-term responses where there is greatest need. These techniques equip planners with a useful and accessible tool to protect vulnerable populations effectively and efficiently with minimal public funds and could advance the policies we use to adapt to a changing climate.
Decision support system for the integration of sustainable parameters in single-family housing project delivery

(Georgia Institute of Technology, 2019-07-26) Tijo, Silvia Juliana

The implementation of sustainable practices in building construction has a direct impact on the financial, environmental, and social dimensions of sustainable development. Powering and heating buildings consumes enormous amounts of energy, and the residential and commercial building sector remains the largest end-use sector for energy in the U.S. The fact that actual energy consumption of this sector is two-fifths of the total energy consumption in the United States represents a significant economic opportunity for the country. In spite of the progress in performance and affordability of sustainable technologies, materials, and systems, the residential sector is behind in adopting these in single-family homes. Several building aspects must undergo evaluation under a holistic approach to achieving the technical and economic success of the project, but the fragmentation of the industry and the required expertise level for using existing simulating tools represent a barrier for this purpose. In residential projects, the selection of design and construction parameters occurs mostly during the early stages of the pre-construction process, while the majority of the building simulation tools require information from late stages of the process. During the early stages, the designer cannot easily predict the impact of decisions on building performance and cost. Furthermore, existing methodologies do not integrate project goals in early stages (i.e., pre-design, conceptual design, and schematic design) of the pre-construction process. Without these methodologies, selecting sustainable parameters for housing delivery and implementing sustainable principles is difficult, and consequently jeopardizes reaching sustainable goals for the building. The result of this research is a decision support system (DSS) that uses the analytic hierarchy process (AHP) and system dynamics (SD) to assist decision makers in the selection of construction parameters for sustainable housing. The proposed DSS integrates a set of project goals in the process of selecting alternatives, allowing a balance between the preferences of the decision maker and the solution that better fits those preferences. The approach focuses more on using DSS to support design exploration rather than finding optimal solutions. Given the iterative nature of the design process and the fragmentation of the construction industry, the proposed DSS provides information about costs, duration, and environmental impact of the alternatives at early stages of the project development. Therefore, an objective comparison of different design alternatives under identical conditions can take place, and the decision maker can learn from the effects of new decisions over other parameters that are interrelated. The outcomes of the research can help developers, architects, and home-owners to define sustainable parameters at early stages of the project delivery when the impact of their decisions is higher, and the cost of implementing changes is lower than in the later stages.
Structures and types of differentiated street grids: The generation, analysis, and sorting of universes of superblock designs

(Georgia Institute of Technology, 2019-05-22) Feng, Chen

The design of urban street networks is critical to how a city looks, feels, and functions. Moreover, the arrangement of streets inside the “superblocks”, which are the large urban areas divided up by the primary street network of the city, gives cities unique characters. This dissertation studies the street network designs at the scale of a square superblock that measures half a mile, or 800 m, on each side—a particularly common dimension for the spacing of arterial streets in the U.S., China, and many other countries. The contemporary urban landscape has been significantly shaped by two distinctive traditions for organizing streets at the scale of a superblock. At one extreme is the deployment of a uniform grid, differentiated only by street widths or intensity of development along the streets. At the other extreme is the “tree-like” pattern in which most separate branches or disjoined enclaves or loops are attached to the main streets, imposing a segregating hierarchy defined by mobility and access. This study explores street network designs that fall between these extremes; the designs in question can be described as differentiated grids. More specifically, we ask: (a) How to create differentiated grids by progressively deforming a square grid? (b) What different kinds of differentiated grids are there? (c) What is the relationship between the different rules that can be applied to creating differentiated grids and the emerging types of differentiation? To study those questions, eight different “syntactic operators” have been developed to progressively deform a street network. For each type of operation, a generative rule/algorithm was created to sequentially apply the operation on a uniform grid up to a specified number of times. An additional generative algorithm was also created to allow operations to be mixed in random sequences. Each generative algorithm was applied to generate a total of 600 differentiated street grids. This resulted in a “design universe” consisting of 5400 differentiated street grids that could be analyzed comparatively and queried for the presence of properties of interest. Such properties include graph connectivity, street density, block size and shape, intersection density, geometric regularity, directional reach, directional distance, and the diversity in syntactic conditions. In addition, the centrality structure of designs was studied. The aim was to formulate and test alternative definitions of “integration cores” and to develop relevant typologies. Consistent with space syntax literature, an integration core is defined as comprising the streets that are closer to all parts of the street network in terms of directional distance. Query algorithms were developed to select designs based on the definitions of alternative types of integration cores. Four main conclusions were reached. First, different types of operations have different capacities to influence the properties of a street network. Second, there are multiple dimensions of differentiation (e.g., differentiation in geometric alignment of streets, differentiation in configurational properties such as DDL, differentiation in block shapes, etc.). In many cases, measures along the different dimensions of differentiation are related. Their predictable relationship can be quantified. Third, while the relationship between different dimensions of differentiation usually has a consistent direction, its slope can vary, depending on the type of operation used to create the differentiation. The variation in slope suggests that properties that may be desirable (for example the creation of a diversified street grid) can be achieved with varying costs regarding properties that may be undesirable (for example the creation of less accessible streets). Fourth, the (local) generative rules used to generate designs do not necessarily lead to specific emergent global properties of the street network of the superblock. Although we cannot predict the specific syntactic type we get by applying a specific generative rule, we know that by applying certain generative rules, we are more likely to generate designs of a specific syntactic type. Thus, the thesis makes two significant contributions to the field of space syntax studies. First, it demonstrates how the systematic generation and querying of universes of designs can be used to rigorously define and enrich key syntactic ideas that have hitherto remained intuitive, such as the ideas of “deformed grid” and the “shape of the integration core”. Second, it demonstrates that in principle, the design of street networks at superblock scale can be studied according to the typologies of interface between local and global integration and according to the typologies of differentiation of the street grid.
Does green infrastructure promote equitable development? The mediating role of social capital in shaping impacts

(Georgia Institute of Technology, 2019-05-17) Fisch, Jessica Ann

Planners, policymakers, and elected officials increasingly view investments in green infrastructure, parks and other green development as opportunities for spurring economic growth, increasing environmental quality, and providing social and recreational amenities in urban areas. However, research has indicated that these projects do not adequately address equity concerns, such as access for low-income and marginalized groups, housing affordability, and displacement of existing residents. Consequently, green infrastructure projects can lead to ‘environmental gentrification. While several works have argued that social capital—the building of relationships, trust, and networks of stakeholders—has the potential to promote more equitable development, the conditions under which more equitable outcomes for green infrastructure projects might be supported and the role of social capital in addressing these concerns has not been adequately examined. This study seeks to clarify the mechanisms through which green infrastructure planning might advance the development of social capital and in turn how social capital influences the housing affordability, gentrification, and community benefits aspects of green infrastructure planning and policy development. The research examines these interrelationships in Atlanta and Washington, D.C., cities with a prominent focus on planning for green infrastructure, high levels of segregation by race and income, and distinct city-wide approaches to coping with gentrification. In clarifying interactions between social capital and green infrastructure planning processes and outcomes, the research enhances our understanding of how social capital might support an increased focus on equity in green infrastructure planning. In particular, the study finds that green infrastructure planning may reinforce social capital, which in turn shapes green infrastructure projects and planning processes with regard to addressing housing affordability and community benefits concerns. It further finds that social capital has served as a catalyst for advocacy and the development of organizations, policies, and programs focused on housing affordability and workforce development. Finally, state and city-level political contexts concerning the goals and tools for promoting housing affordability and community benefits shape the ability of municipal and neighborhood-level actors to address equity concerns associated with green development. These findings support several recommendations for policy and planning to promote more equitable development surrounding green infrastructure projects and planning processes.
A framework for developing machining learning models for facility life-cycle cost analysis through BIM and IoT

(Georgia Institute of Technology, 2019-04-02) Gao, Xinghua

This thesis presents a research project that developed a machine learning-enabled facility life-cycle cost analysis (LCCA) framework using data provided by Building Information Models (BIM) and the Internet of Things (IoT). First, a literature review and a questionnaire survey were conducted to determine the independent variables affecting the facility life-cycle cost (LCC). The potential data sources were summarized, and a data integration process introduced. Then, the framework for developing machine learning models for facility LCCA was proposed. A domain ontology for machine learning-enabled LCCA (LCCA-Onto) was developed to encapsulate knowledge about LCC components and their roles in relation to sibling ontologies that conceptualize the LCCA process. A series of experiments were conducted on a university campus to demonstrate the application of the proposed machine learning-enabled LCCA framework. Finally, the author’s vision of the future smart built environment was discussed.
Modeling and predicting the variation of US highway construction cost

(Georgia Institute of Technology, 2019-03-25) Cao, Yang

The U.S. government attaches great importance to highway construction every year. Because of the importance of highway construction projects and the tremendous expenditure, the budgeting and cost control is a significant job for all federal agencies and state Department of Transportation (DOTs). A major problem for highway construction costs is that they exhibit a significant variation over time such as the highway construction cost indexes (HCCI), and it is mainly caused by a complex interactive effect such as market-related and project-specific factors. The variation hinders the estimators to catch the correct trend of the market and thus poses a challenge for both owners, such as state, local Department of Transportation, and contractors, in correct budgeting and cost estimating. The main objective of this PhD research is to explore the explanatory variables and develop machine learning methods to model and predict highway construction cost and examine the accuracy of the forecasting results with those of the existing methods such as regression analysis and time series models. The major promising feature of the proposed methods over existing ones is the capability to explain the non-linear relation, which is prevailing in practice. Besides, there is no restriction for the proposed models, compared to linear regression, which assumes the linear relation and normal-distributed-error, and time series analysis, which requires the stationarity of data before analysis. The dissertation summarizes the results from two parts of the research: (1) Univariate time series index prediction in Long Short-Term Memory; (2) Modeling the unit price bids submitted for major asphalt line items in Georgia, project-specific and macroeconomic factors. A deep learning model based on the recurrent neural network will be developed due to its strength in long term memory and catching the variation of the data. The Texas HCCI is used as an illustrative example because Texas reports a frequent volatile index. The performance of the model will be tested in different prediction scenarios (short-term, midterm and long-term). A dataset containing fifty-seven variables with potential power to explain the variability of the submitted unit price bid are collected in this study. These variables represent a wide range of factors in six aspects: project characteristics, project location and its distance to major supply sources for critical materials, level of activities in the local highway construction market, overall construction market conditions, macroeconomic conditions, and oil market conditions. The machine learning feature selection algorithm Boruta analysis will be used to select the most significant features with the greatest capability to predict the unit price bid for asphalt line items. The partial dependence plots endow the explanatory power to the developed machine learning models. An ensemble learning model will be constructed based on the selected features to forecast the unit price bid. The accuracy of the predicted machine learning models will be compared and validated with the existing multiple regression model and the Monte Carlo Simulation. The main contribution of this research to the body of knowledge in cost forecasting are be summarized from three aspects: first, the research developed a new set of machine learning models that provide more accurate costs forecasts compared to the existing methods. For example, the non-linear machine learning methods are more accurate than the time series models which are frequently used in the former research. In the field of cost research, a small improvement in model accuracy results in a significant amount of actual impact in budget estimation. Second, the modified encoder and decoder architecture performed well in numerical time series data prediction problem. Instead of making one sequence of output, the roll-forward forecasting turned out to be more accurate. Third, from the practical application perspective, the proposed machine learning models can handle a wide range of issues with the input data that are common in the field of highway construction cost forecasting, such as missing values. Another practicality contribution is that methods in this research are applicable to big data which is an industry trend, while most former models were developed based on a small dataset. The research also proposed a construction cost database which will largely provide the convenience for easy utilization of the model. Fourth, the research identified the most significant features to forecast the variation of unit price bids of resurfacing projects in Georgia, and the analysis laid emphasis on the explanatory power of prediction models. With the improved prediction capability, state DOTs can benefit from the proposed models in preparing more accurate budgets and cost estimates for highway construction projects. The analysis process and proposed models in research are also applicable to other time series data prediction and cost estimating problems.
A framework for coordinated models of architectural precast concrete facades

(Georgia Institute of Technology, 2019-02-28) Collins, Jeffrey

Architects are often unaware of details, constraints, and variables that define and deliver architectural components. Many factors such as constructability, budget, or scheduling commitments, force changes to design concepts – potentially resulting in time-consuming redesign or loss of design aspirations – because incorporation of fabrication and expert knowledge occurs too late in the process. At the same time, fabricators, obligated to re-model these components – typically via error-prone manual translation – may be unaware of critical architectural properties envisioned but difficult to represent in design intent documents. The focus of this dissertation is to establish a new framework for coordination among project actors, linking currently disparate global and local descriptions of architectural intent and corresponding components via parametric digital models, with the aim of improving representations, enabling more informed conversations, and streamlining exchanges during early stages of design. In order to show the potential of this framework, research is focused on architectural precast concrete façades. Building façades are especially relevant to both architectural theory and practice as they are critical to a buildings’ character but remarkably complex in assembly. The architectural precast façade offers, in particular, a system whose parts are discreet through surface panelization, customizable via extensive features, and fundamental to the overall buildings’ aesthetic. Protocols and techniques for generating and linking customizable digital models for coordination are documented for a variety of surface patterns and panel feature types found in precedent buildings with architectural precast concrete façades. These models are used to demonstrate the process of developing parametric maps, both as a means of engaging issues of fabrication in early stages of design as well as to demonstrate benefits of incorporating such maps in future state workflows. Knowledge gained from recording various processes undertaken, conversations held, and documents produced by precast fabricators during the shop drawing phase of their work informs the parametric maps from both global and local perspectives. The strategies from the precedent analysis are then implemented through the exploration of design and fabrication issues raised by novel student proposals. The research suggests that the current disconnect between architectural intent and fabrication knowledge contributes to limited design exploration, and ultimately, reduces use of architectural precast concrete façades and furthermore, that linked digital models can stimulate interaction between designers and fabricators – bridging currently disparate workflows and value systems – while simultaneously enabling design exploration, incorporating fabrication details, and allowing new opportunities for precast buildings to emerge.
Sustainable energy technology, adoption, rebound, and resilience

(Georgia Institute of Technology, 2019-01-22) Hashemi Toroghi, Shahaboddin

While in the United States, centralized generation and distribution network are the basis of the current electric infrastructure, the recent surge in uptake of solar photovoltaic (PV) systems introduces a new avenue to decentralize this system. Furthermore, PV systems can substitute the grid electricity and increase the share of renewable energy sources. While by 2018, five states in the U.S. (California, Hawaii, Nevada Massachusetts, and Vermont) could reach 10% threshold for the share of solar sources in generating electricity, at the country level this share is still less than 3%; whereas in some other countries, such as Germany and Japan, it has already reached more than 6%. This dissertation examines the diffusion of PV systems from three perspectives, addressing three gaps in knowledge: an empirical study of the diffusion of PV systems in Georgia, a method to estimate renewable rebound effect, and a framework to quantify the resilience capacity of an electric infrastructure system with emergency electricity generators, including PV systems. Three studies present the primary contributions of this research. Study 1 examines the diffusion of PV systems in Georgia, identifies characteristics of adopters and patterns of adoption, and forecasts the future adoption of PV systems. Study 2 introduces a new approach to estimate the direct rebound effect, subsequent of a major adoption of PV systems. Study 3 presents a state-of-the-art framework that quantifies the resilience capacity of an electric infrastructure system with emergency electricity generators. The findings of the study 1 provides a benchmark for the future adoption of PV systems and highlights the impact of socio-economic and location-based factors in the diffusion of PV systems in Georgia. These findings can be used to shape a more effective policy, aiming to increase the share of PV systems, or evaluate the effectiveness of a policy. The finding of the study 2 opens a new avenue to compute the rebound effect and can support development of a policy to mitigate the renewable rebound effect in a targeted region. The finding of the study 3 can help system designers to customize the design of a resilient system based on its characteristics. The introduced framework can further be used to investigate improvement of the resilience capacity in an electric infrastructure system by increasing the penetration of PV systems, or other decentralized electricity generators in a region.
Embodied life cycle assessment and potential environmental impacts of improvement options for detached single-family houses in Atlanta

(Georgia Institute of Technology, 2019-01-11) Shirazi, Arezoo

20% of US energy consumption and the consequential environmental impacts are associated with the building sector. Previous studies showed that approximately 30% of a building's life cycle energy is attributed to its embodied energy. The residential housing market alone has a significant impact on US emissions. According to a recent report from the Washington Post, detached single-family houses represent the most common style of housing in major US cities and it is close to 40% for Atlanta. This study focuses on residential buildings in the Atlanta metropolitan area. The overarching objective of this research is to include the changes of building construction methods and building energy codes into an embodied Life Cycle Assessment (LCA) model to evaluate the long-term impacts of improvement options for the residential buildings in the region. The primary contributions of this research are: (1) benchmarking the generic characteristics of existing residential buildings considering building codes and construction changes in the region; (2) investigating the trend of embodied energy and emissions of benchmarked buildings considering the 1970s transition in the construction industry; and (3) identifying potential improvement options for benchmarked buildings and comparing the embodied energy and environmental impacts of identified options. The main findings of this research showed: (1) lower embodied energy and environmental impacts per unit area for houses built before 1970s; (2) lower embodied energy and impacts per unit area for 2-story houses; (3) a range of 1.8 to 3.9 Gj/m2 embodied energy for residential buildings in the region; (4) highest environmental impacts for attic/knee insulation and heating, ventilation and air conditioning (HVAC) units replacement through retrofitting residential buildings; and (5) significant environmental impacts for foundation wall insulation and window upgrading through retrofitting dwellings built before the 1970s. The results of this research highlight the role of the life cycle approach for selecting low emission options during the design and implementation of construction and retrofit actions for residential dwellings. The results could further be used to investigate the potential improvement options for an optimum energy usage while reducing life cycle emissions by renovating existing residential buildings in a region.
Red hot American summer: Extreme heat and physical activity of adults

(Georgia Institute of Technology, 2018-12-20) Lanza, Kevin

This dissertation investigates the relationship between extreme summer heat and outdoor, indoor, and total (i.e., outdoor + indoor) physical activity levels of US adults. With the lack of physical activity across the US, public health practitioners and city planners are making concerted efforts to promote physical activity through formal interventions and the design of spaces, respectively. To inform physical activity interventions, researchers examine which factors associate with physical activity, one of which is temperature. The majority of studies exhibit a significant positive association between temperature and physical activity, yet no studies examine exceptionally hot summer days, which disproportionately impact cities and are set to become more prevalent in the future. This dissertation tests three novel questions: 1) how do hot days associate with outdoor, indoor, and total physical activity; 2) how do hot days influence the effect of built environment factors on outdoor physical activity; and 3) how do heat waves – consecutive hot days – associate with outdoor, indoor, and total physical activity? This work made use of self-reported physical activity and demographic data collected during summer 2016 for a National Science Foundation project (NSF award number: 1520803). The study sample included a spatial and demographic mix of ~50 adults per study city (i.e., Atlanta, Detroit, and Phoenix). Heat was measured as both hot days and heat waves (i.e., two or more consecutive hot days), utilizing air temperature and relative humidity data collected at each city’s major airport. The examined built environment factors (i.e., density, safety, trees, hilliness, connectivity, access to parks, and access to shops + services) were primarily collected from government sources and calculated within an 800m Euclidean distance of each study participant’s home address. Separate two-level growth curve models were run for each research question, version of the dependent variable (i.e., Any Activity and Recommended Activity), and location of physical activity (i.e., outdoor, indoor, and total). Multilevel modeling predicted that 1) hot days do not exhibit a significant association with indoor, outdoor, or total physical activity; 2) hot days do not significantly influence the effect of built environment factors on outdoor physical activity; and 3) heat waves do not exhibit a significant association with outdoor, indoor, or total physical activity. These findings refute the study hypotheses that extreme summer heat would decrease outdoor and total physical activity, while shifting physical activity to indoor, thermally comfortable environments. With high temperatures potentially not serving as a barrier to physical activity, cities should allocate resources to reducing the risk of exertional heat illness, an adverse health event expected to become more frequent with physical activity promotion and climate change.