Doctor of Philosophy with a Major in Building Construction

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Doctor of Philosophy with a Major in Building Construction

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https://hdl.handle.net/1853/72547

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Degree Series

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School of Building Construction

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Now showing 1 - 10 of 34

Blockchain-enabled Smart Contract System for Creating System-based Trust in Subcontracting Process

(Georgia Institute of Technology, 2023-03-27) Yoon, Jong Han

The unethical practices of bid shopping and peddling during the subcontractor procurement process can reduce trust between the general contractor (GC) and subcontractors (Subs) and lead to low-quality work, claims and disputes, schedule delays, and cost overruns. Despite the adverse impacts of these unethical practices on construction projects, the construction industry still lacks an ethical and trustworthy subcontracting process to prevent bid shopping and peddling. Furthermore, the transactional relationships between the GC and Subs in construction projects make profit-driven pursuits tempting, thereby increasing opportunistic behaviors. This dissertation contributes to the body of knowledge by developing a framework based on a blockchain-enabled smart contract system to address these unethical practices, thus establishing the subcontracting process grounded on system-based trust. Blockchain provides tamper-proof and decentralized data storage, and smart contracts enable an automatic contract execution by leveraging the data stored in Blockchain. The proposed framework employing the above advantages is demonstrated through a pilot test, and its feasibility and effectiveness are validated through a survey with nine professionals who had sufficient years of experience in the construction industry. The validation results show that the framework can prevent the aforementioned unethical practices and enable Subs to fairly compete for bid awards with proper budgets. In addition to the development of a subcontracting process leveraging a blockchain-enabled smart contract system, this dissertation contributes to the body of knowledge by providing a game-theoretic framework that the GCs and Subs can use to quantify and evaluate the outcomes of their strategic behaviors (e.g., trust-driven vs profit-driven behaviors) in the subcontracting process. Game theory in the framework enables mathematically analyzing and comparing the payoffs of strategic behaviors, using Nash Equilibrium. This dissertation also contributes to the body of knowledge by empirically verifying the effects of system-based trust created by a blockchain-enabled smart contract system on GCs’ and Subs’ strategic behaviors by conducting role-playing simulations. The developed game-theoretic-framework-based analysis of the simulations demonstrates that the blockchain-enabled smart contract effectively promotes trust-driven behaviors by enhancing system-based trust, thereby leading to a win-win game for the GC and Subs in the subcontracting process. These valuable findings establish the foundation for a transformative subcontracting process that is more ethical and grounded on system-based trust. Moreover, the findings can help the construction industry deepen its understanding of the significance of trust-driven behaviors in the subcontracting process. The findings also promote the enforcement of trust-driven behaviors by enhancing system-based trust through blockchain technology.
Enhancing Organizational Transformation for Design-Build Infrastructure Projects: Design Liability, Construction Quality Assurance, and New Engineering Leadership Requirements

(Georgia Institute of Technology, 2022-07-29) Lee, Jung Hyun

Major transportation infrastructure projects have used alternative project delivery, such as design-build (DB), to streamline and expedite project delivery, transferring many roles and responsibilities from state departments of transportation (DOTs) to private actors. One challenge that state DOTs face in their major DB projects is ensuring that the DB team upholds the highest standards of design and construction quality in the integrated design and construction environment. The overarching objectives of this study are to support decision-makers in streamlining project delivery by identifying challenges related to understanding gaps between public owners' expectations and the industry's perception and suggesting recommendations to mitigate the gaps. Most specifically, this study addresses issues found in DB transportation infrastructure projects and recommends innovative solutions to overcome those issues in the following areas: (1) design liability, (2) construction quality assurance, and (3) a new engineering leadership requirement on the DB team. This study utilizes a mixed-method research methodology, combining quantitative and qualitative techniques to identify key areas of variances in the integrated DB infrastructure projects. The data in this study come from a survey and semi-structured interviews. Because of the interdisciplinary nature of the research, it is necessary to capture several viewpoints from a wide range of subject-matter experts (SMEs) from multiple domains, including design consultants, highway contractors, public owners, owner representatives, insurance and legal advisors, and construction engineering and inspection (CEI) specialists. The results show that SMEs had considerably different perceptions regarding the frequency and severity of design claim sources in the DB environment. Inconsistencies between CEI perceptions and DOT requirements for quality assurance roles and responsibilities are identified. The results also highlight that a new engineering leadership requirement on the DB team will add value to large and complex projects. This study contributes to the body of knowledge in proactive design and construction quality management by providing decision-makers insights into design liability issues and opportunities to reduce them, providing guidance on reinforcing the quality assurance program for current and future DB projects, and mitigating gaps between the DOT's expectations and the industry's perceptions. The findings of this study have important implications for future practice and offer constructive guidance on streamlining project delivery in the DB transportation infrastructure market.
DISADVANTAGED BUSINESS ENTERPRISES: EFFECT OF DECERTIFICATION AND COMPETING IN THE GEORGIA TRANSPORTATION CONSTRUCTION MARKETPLACE

(Georgia Institute of Technology, 2021-04-29) Horsey, Irish L.

The U.S. Department of Transportation (DOT) allocates billions of dollars annually for transportation projects. State Departments of Transportation (SDOT) that receive federal assistance for transportation contracting must meet the requirements of the Code of Federal Regulations (CFR) Title 49: Transportation Part 26 (ECFR, 2016). This regulation ensures that all business enterprises have fair opportunities for federally funded transportation contracting. Therefore, SDOTs are mandated to develop DBE goals for participation of firms, certification of DBE firm eligibility, evaluation of their DOT-assisted contracts for compliance with goals to ensure nondiscrimination in federally assisted procurement. There are eight primary objectives for the DBE program. One of which is to assist the development of firms that can compete successfully in the marketplace outside the DBE program. The DBE program has been a source of controversy since its inception (La Noue, 2008). Research shows that both DBE and non-DBE firms have grievances with the effectiveness of the overall program. Some also believe that the program creates a dependency of its participant and that inputs of knowledge would assist with the growth and development of firms to become independent contractors outside of the program (Beliveau et al., 1991). A number of factors have been presented by prior research that hinder the growth and development of certified DBE firms with a focus on performance, internal impediments, and external impediments of the program. However, there is minimal data on the preparation of DBE firms by SDOTs and their ability to compete in the open market outside of the DBE program. There is value in a study that evaluates the DBE program to determine if it is meeting the referenced objective. This research analyzes the participants of the DBE program and factors that contribute to the decertification of firms and affect their growth and development. Evaluation of certified DBEs, decertified DBEs and program administrators on this specific program objective contributes new data to the body of knowledge. The objective of this study is to evaluate the GDOT DBE program and that of similar SDOTs to determine if the DBE program in Georgia is assisting with the development of firms to compete in the marketplace. The main contribution of this research is to identify factors that assist the growth and development of DBE construction firms who voluntarily decertify and compete independently in the open market and explore the issues of certified firms that prohibit graduation. There are three outcomes of this study that contribute to the body of knowledge: regression models, development and decertification factors, and program administrator recommendations. The results of this research reveal if the program is meeting this objective for Georgia construction transportation projects based on factors obtained from the data analysis. The findings offer improvement to policy regarding the DBE program and government contracting for construction transportation projects.
DECISION SUPPORT FRAMEWORK FOR TRANSFORMING URBAN BUILDINGS AT MULTIPLE SCALES

(Georgia Institute of Technology, 2020-04-25) Chang, Soowon

Due to the increasing population, cities are requiring more energy. Among urban elements, buildings account for about 40% of energy demands and 30% of carbon dioxide emissions globally. To address the increase of energy demands and environmental responsibility, existing buildings should be transformed into highly energy efficient forms. This research explores how to support decisions that affect performance-driven smart and resilient urban systems focusing on building renovations. The research scope covers the redevelopment of existing built forms at multiple scales. Since urban objects influence urban patterns at other scales, both individual and collective performances of buildings at larger scales should be evaluated to support proper redevelopment decisions. In addition, the transformation of existing buildings will encounter different problems and challenges at different scales in urban areas. On an individual building level, the selection of different envelope options can project the future architectural environment of buildings. On a block level, the performance will be changed along with combinations of building typologies such as land use, height, floor area, etc., and therefore changes to building typologies should be managed collectively to improve the performance. When PV are applied in buildings and hourly electricity demands are recognized, the dynamic energy flows on a community level will become complex to manage. In this respect, this research is devised to identify and address redevelopment problems at different scales: individual buildings, block, and community. On the individual building level, this research studies how to support decision-making when optimizing the selection of building envelopes by using a Genetic Algorithm (GA). Based on the findings from optimizing at each scale, an interdependence of building parameters and multiple performance is observed. Therefore, decision frameworks across multiple scales are extrapolated to support community-driven and building-driven decisions. On the block level, this research explores how existing building typologies influence multiple performance indicators in a collective manner to support reconfiguring decisions using a Bayesian Multilevel Modeling. On the community level, this study addresses how the community can optimize block boundaries for resiliently managing the energy demand and supply of groups of buildings by using K-nearest neighbors (KNN) and community clustering algorithms. This research will contribute to making appropriate decisions for investment, regulations, or guidelines when renovating physical building assets at different scales in urban areas. The research findings will consolidate theoretical understandings about the relationships between building design and construction parameters considering multiple performance indicators at multiple scales in urban areas. Since many cities are at the tipping point trying to become more resilient, increasingly focusing on sustainability, economic feasibility, and human well-being, a better understanding of the impact of built forms at multiple scales will support urban development decisions for the future smart and connected communities.
Clash Resolution Optimization based on Component and Clash Dependent Networks

(Georgia Institute of Technology, 2020-04-25) Hu, Yuqing

Effective coordination across multi-disciplines is crucial to make sure that the locations of building components meet physical and functional constraints. Building information modeling (BIM) has been increasingly applied for coordination and one of its most widely used applications is automatic clash detection. The realistic visualization function of BIM helps reduce ambiguity and expedites clash detection. However, many project participants criticize automatic clash detection, as many detected clashes are irrelevant with no significant impact on design or construction work, thereby decreasing the precision of clash results and the benefits of BIM. In addition, clash detection consists of discovering problems, but it does not entail solving these clashes. Even though some studies discussed automatic clash detection, they rarely discussed the dependence relationships between building components. However, a building is an inseparable whole, and the dependent relationships among building components propagate the impact of clashes. Relocating one object to correct one clash may result in other objects violating spatial constraints, which may directly cause new clashes or indirectly cause them through relocating other components. Therefore, figuring out the dependency among clash objects with peripheral building components is useful to optimizing clash solutions by avoiding change propagation. Algorithms are designed to automatically capture dependency relations from models to construct a component dependency network. The network is used as an input to distinguish irrelevant clashes for improving clash detection quality by analyzing the relations between clash components and the relations between clash components with their nearby components. The feasibility to harness the clash component network and graph theory are also explored to generate the clash component change list for minimizing clash change impact from a holistic perspective. In addition, this study demonstrates how to use BIM information to refine clash management, and specifically focus on designing a hybrid clash correction sequence to minimize potential iterative adjustments. The contributions of this study exist at three levels. The most straightforward contribution is that this research proposed a method to improve clash detection quality as well as to provide decision support for clash resolution, which can help project teams to focus on important clashes and improve design coordination efficiency. In addition, this research proposes a new perspective to view clashes, switching the clash management focus and inspiring researchers to focus on finding global optimal solutions for all clashes other than a single clash. The third level is that even though this research focuses on clash management, the optimization algorithms based on graph theory can be used in other interdependent systems to improve design and construction performance.
A user-centered analysis of virtual reality in design review: Comparing three-dimensional perception and presence between immersive and non-immersive environments

(Georgia Institute of Technology, 2020-01-03) Paes, Daniel

Over the last few years, the adoption of Virtual Reality (VR) solutions by the construction industry has grown rapidly worldwide. These have been developed and used for different purposes, including collaborative design review. Nonetheless, the extent to which such systems enhance the cognitive capabilities of construction professionals involved in the design review activity is still unclear. Knowledge on the cognitive benefits provided by Immersive Virtual Reality (IVR) technology is essential to elicit its usefulness and effectiveness, as well as to provide development directions. In this context, this study sought to quantitatively verify the ability of an IVR system in providing users with enhanced three-dimensional (3D) perception of a BIM (Building Information Modeling) model and greater levels of presence in the virtual environment (VE) compared to a non-immersive conventional VR system. The method compares users’ 3D perception and levels of presence between two modes of presentation (IVR vs. non-immersive VR). The study also examines the relationship between 3D perception and presence within each virtual environment. Controlling for individual factors and order effects, findings indicate that in comparison to a conventional workstation, IVR technology improves 3D perception of the architectural model and provides more immersive experiences. Results also suggest no association between 3D perception and presence in virtual environments, contrary to expectations. The ability of IVR technology in providing current and future workforce with a significantly better understanding of the three-dimensional relationships of architectural models and greater levels of presence in the review task is expected to benefit collaborative design review.
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.
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.