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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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search.filters.applied.f.advisor: Ashuri, Baabak × End date: 2023 × Start date: 2020 × search.filters.applied.f.isOrgUnitOfPublicationTitle: School of Building Construction ×

Publication Search Results

Now showing 1 - 2 of 2
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    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.
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    Industry 4.0 And Short-Term Outlook for AEC Industry Workforce
    (Georgia Institute of Technology, 2021-12-13) Quintana, Emilio
    Technology is uniquely transforming our society to a significant degree. This transformation has been described as Industry 4.0 and encompasses machine learning, computerization, automation, artificial intelligence, and robotics. Industry 4.0 is currently impacting the United States’ workplace and is projected in continue uniquely changing our society over the next twenty years or so. Looking specifically at the AEC industry, this paper researches how the AEC industry workplace could be impacted by Industry 4.0 over the next several years. The hypothesis that jobs more at risk for automation should see low or negative growth and lower wages over the next several years was tested by using U.S. Bureau of Labor Statistics (BLS) occupational wage data and growth projections to create an opportunity value for each occupation, and then evaluating the relationship between the opportunity value and probability of automation. A statistical significance was found between the two variables. The hypothesis that certain skills are particularly associated with high growth/high wage jobs versus low growth/low wage jobs was tested by scraping important skills/qualities from the individual occupational webpages hosted by the U.S. Bureau of Labor Statistics, and then comparing the approximately top 80% of skills scraped between the two groups. Certain skills/qualities were found to be particularly associated with each group. Finally, the occupations associated with the AEC industry were compared with the findings from the first two hypotheses. The discoveries were that the AEC industry is potentially more susceptible to Industry 4.0 than other industries. This research is of significance because research into how the AEC industry workplace will be impacted by Industry 4.0 over the next several years was not found in the research background, and it has implications on potential career choices, skill requirements, and areas of research and development. Recommendations for future work include utilizing new data sources, Monte Carlo simulations, cohort analysis, and cluster analysis to make more specific forecasts on Industry 4.0’s impact on the AEC industry.