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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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    Human-building interaction: Supporting students’ performance and wellbeing through built environments on campus
    (Georgia Institute of Technology, 2022-05-03) Kim, Yujin
    Facility management aims to ensure buildings' quality and components to support occupants in achieving their goals and objectives. Campus environments play a vital role in student success by providing supportive spaces for learning, living, resting, and socializing. However, studies about the built environment of higher education have mainly focused on the ways of learning and teaching instead of physical components, and built environments on campus and their effects on students have been little studied. This study aims to 1) propose and investigate a theoretical framework on the relationship between built environments and students’ outcomes (i.e., academic performance and wellbeing) in higher education and 2) identify the preferred physical and functional environments on campus depending on student activities. This study proposed a theoretical framework based on the socio-materiality theory to explain the complex relationship between materiality and social practice in built environments. The proposed framework was tested in three-fold. First, study 1 investigated how students’ space usage of a library changed after the COVID-19 pandemic and was related to indoor environmental features. Data were collected via survey with 66 responses in pre-pandemic and interviews with 12 students during the pandemic. One of the main findings was that, even though students used the library less during the pandemic, they expected to use it as much as pre-pandemic or even more after the pandemic. Furthermore, students required different environmental features depending on their purpose of space usage, and the physical environment cultivated a sense of belonging and community. Second, study 2 tested the restorative effect in indoor settings using an eye-tracking device. Data were collected through a true experiment with 34 students randomly assigned to biophilic vs. non-biophilic design settings. The findings indicated that biophilic design itself was not decisive to restorative effects. Students in both settings selectively looked at nature-like (natural material) and views of nature and reported restoration effects. Lastly, study 3 analyzed how multi-dimensional environments (i.e., physical and functional environments) affected students’ outcomes in dormitories. A total of 128 self-reported survey responses revealed that the physical and functional environments were related to each other and directly and indirectly affected students’ perceived learning performance and wellbeing. In conclusion, this thesis provides a theoretical framework to explain the iterative process of physical and functional environments on campus and empirical evidence of the importance of built environments for enhancing student experiences and supporting different activities, such as learning, collaborating, socializing, and resting. For this, academic leadership, building managers, and designers should actively adopt the evidence-based design approach to provide appropriate environments and support student activities.
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    Analyzing Physical Workplace and Service Management Using Natural Language Processing and Machine Learning Approaches
    (Georgia Institute of Technology, 2022-04-26) Hong, Sungil
    The demand for workplace flexibility has emerged according to ever-changing environments, such as sharing and gig economy, alternative work arrangement, and COVID-19. This study proposes a redefined facility management model corresponding to the changing circumstances, which provides not only space but also activity support and leisure services. Coworking space (CWS) is one of the embodiments of the model. This research aims to develop CWS management strategies for 1) user preferences in physical workplace environments and services during COVID-19 and 2) data management methods utilizing natural language processing (NLP) and machine learning techniques. Two main studies in this research address three research objectives: 1) identifying preferences for facilities and services factors in CWSs during COVID-19; 2) detecting changing preferences for factors about facilities and services during COVID-19; 3) proposing the applications of machine learning and NLP techniques and demonstrating the applicability of computational data collection and analysis methods in the physical workplace management research. First, Study I proposes a thematic categorization scheme of CWS spatial and service factors and elements. Based on the categories, a mixed-method approach was utilized for the comprehensive data analysis, including content analysis, classification, and clustering. The results show that CWS users have become sensitive to disruptive behaviors and hygienic responses to infectious diseases after the pandemic. The findings also present a need for a sense of community and various technology needs for virtual interactions. Second, Study II performed the data integration of a large computerized maintenance management system dataset of a public college campus into a single CWS building maintenance dataset to build robust machine learning-based text classification models for a small dataset. The results show the qualitative and quantitative increase in prediction performance of text classifications. Study II implies that data integration will accelerate smart facility management, including small or single buildings, by sharing public datasets. In conclusion, this research sheds light on online big data collection and analysis in physical workplace management research. It also presents how the facility management industry can apply such state-of-the-art technology in utilizing historical data to make data-driven decisions.