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- Creators: Sullivan, Kenneth
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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
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