ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 3 of 3
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- All Subjects: infrastructure
- Creators: Sullivan, Kenneth
- Creators: York, Abigail M
Description
By the evolution of technologies and computing power, it is possible to capture and save large amounts of data and then find patterns in large and complex datasets using data science and machine learning. This dissertation introduces machine-learning models and econometric models to use in infrastructure transportation projects. Among transportation infrastructure projects, the airline industry and highways are selected to implement the models.The first topic of this dissertation focuses on using machine-learning models in highway projects. The International Roughness Index (IRI) for asphalt concrete pavement is predicted based on the 12,637 observations in the Long-Term Pavement Performance (LTPP) dataset for 1,390 roads and highways in the 50 states of the United States and the District of Columbia from 1989 to 2018. The results show that XGBoost provides a better model fit in terms of mean absolute error and coefficient of determination than other studied models. Also, the most important factors in predicting the IRI are identified.
The second topic of this dissertation aims to develop machine-learning models to predict customer dissatisfaction in the airline industry. The relationship between measures of service failure (flight delay and mishandled baggage) and customer dissatisfaction is predicted by using longitudinal data from 2003 to 2019 from the U.S. airline industry. Data was obtained from the Air Travel Consumer Report (ATCR) published by the U.S. Department of Transportation. Flight delay is more important in low-cost airlines, while mishandled baggage is more important in legacy airlines. Also, the effect of the train-test split ratio on each machine-learning model is examined by running each model using four train-test splits. Results indicate that the train-test split ratio could influence the selection of the best model.
The third topic in this dissertation uses econometric analysis to investigate the relationship between customer dissatisfaction and two measures of service failure in the U.S. airline industry. Results are: 1) Mishandled baggage has more impact than flight delay on customer complaints. 2) The effect of an airline’s service failures on customer complaints is contingent on the category of the airline. 3) The effect of flight delay on customer complaints is lower for low-cost airlines compared to legacy airlines.
ContributorsDamirchilo, Farshid (Author) / Fini, Elham H (Thesis advisor) / Lamanna, Anthony J (Committee member) / Parast, Mahour M (Committee member) / Sullivan, Kenneth (Committee member) / Arizona State University (Publisher)
Created2021
Description
Cities are increasingly using nature-based approaches to address urban sustainability challenges. These solutions leverage the ecological processes associated with existing or newly constructed Urban Ecological Infrastructure (UEI) to address issues through ecosystem services (e.g. stormwater retention or treatment). The growing use of UEI to address urban sustainability challenges can bring together teams of urban researchers and practitioners to co-produce UEI design, monitoring and maintenance. However, this co-production process received little attention in the literature, and has not been studied in the Phoenix Metro Area.
I examined several components of a co-produced design process and related project outcomes associated with a small-scale UEI project – bioswales installed at the Arizona State University (ASU) Orange Mall and Student Pavilion in Tempe, AZ. Specifically, I explored the social design process and ecohydrological and biogeochemical outcomes associated with development of an ecohydrological monitoring protocol for assessing post-construction landscape performance of this site. The monitoring protocol design process was documented using participant observation of collaborative project meetings, and semi-structured interviews with key researchers and practitioners. Throughout this process, I worked together with researchers and practitioners to co-produced a suite of ecohydrological metrics to monitor the performance of the bioswales (UEI) constructed at Orange Mall, with an emphasis on understanding stormwater dynamics. I then installed and operated monitoring equipment from Summer 2018 to Spring 2019 to generate data that can be used to assess system performance with respect to the co-identified performance metrics.
The co-production experience resulted in observable change in attitudes both at the individual and institutional level with regards to the integration and use of urban ecological research to assess and improve UEI design. My ecological monitoring demonstrated that system performance met design goals with regards to stormwater capture, and water quality data suggest the system’s current design has some capacity for stormwater treatment. These data and results are being used by practitioners at ASU and their related design partners to inform future design and management of UEI across the ASU campus. More broadly, this research will provide insights into improving the monitoring, evaluation, and performance efficacy associated with collaborative stormwater UEI projects, independent of scale, in arid cities.
I examined several components of a co-produced design process and related project outcomes associated with a small-scale UEI project – bioswales installed at the Arizona State University (ASU) Orange Mall and Student Pavilion in Tempe, AZ. Specifically, I explored the social design process and ecohydrological and biogeochemical outcomes associated with development of an ecohydrological monitoring protocol for assessing post-construction landscape performance of this site. The monitoring protocol design process was documented using participant observation of collaborative project meetings, and semi-structured interviews with key researchers and practitioners. Throughout this process, I worked together with researchers and practitioners to co-produced a suite of ecohydrological metrics to monitor the performance of the bioswales (UEI) constructed at Orange Mall, with an emphasis on understanding stormwater dynamics. I then installed and operated monitoring equipment from Summer 2018 to Spring 2019 to generate data that can be used to assess system performance with respect to the co-identified performance metrics.
The co-production experience resulted in observable change in attitudes both at the individual and institutional level with regards to the integration and use of urban ecological research to assess and improve UEI design. My ecological monitoring demonstrated that system performance met design goals with regards to stormwater capture, and water quality data suggest the system’s current design has some capacity for stormwater treatment. These data and results are being used by practitioners at ASU and their related design partners to inform future design and management of UEI across the ASU campus. More broadly, this research will provide insights into improving the monitoring, evaluation, and performance efficacy associated with collaborative stormwater UEI projects, independent of scale, in arid cities.
ContributorsSanchez, Christopher A (Author) / Childers, Daniel L. (Thesis advisor) / Cheng, Chingwen (Committee member) / York, Abigail M (Committee member) / Arizona State University (Publisher)
Created2019
Description
The objective of the study was to examine the impact construction document deficiencies have on heavy/civil low-bid infrastructure projects. It encompasses the expertise of 202 heavy/civil construction professionals comprised of contactors and public project owners. The study was designed to determine the frequency and timing of when a contractor discovers construction document deficiencies on heavy/civil low bid projects. The information was correlated with further study data of when a contractor ultimately reports the discovered construction document deficiencies to the public project owner. This research data was compiled and analyzed to determine if contractors are withholding construction document deficiencies from public owners until after the project contract has been executed. The withholding of document deficiencies can benefit contractors by resulting in additional owner incurred costs and potential justification for project time extensions. As a result, further research was required to examine the impact construction document deficiencies have on project cost and schedule. Based on the study findings, it has led to the development of a Contractor Document Review Assessment. The Contractor Document Review Assessment is a risk mitigation device in which contractors and public project owners can identify construction document deficiencies on heavy/civil low-bid construction projects before the project contract has been executed.
ContributorsPesek, Anthony Edward (Author) / Sullivan, Kenneth (Thesis advisor) / Badger, William (Committee member) / Bingham, Evan (Committee member) / Arizona State University (Publisher)
Created2017