Search Content

Matching Items (2)

Use of Machine Learning and Data Science on Infrastructure Transportation and Construction Projects

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

Multi-Scale Characterization of Bitumen Doped with Sustainable Modifiers

Description

This research is a comprehensive study of the sustainable modifiers for asphalt binder. It is a common practice to use modifiers to impart certain properties to asphalt binder; however, in order to facilitate the synthesis and design of highly effective sustainable modifiers, it is critical to thoroughly understand their underlying molecular level mechanisms in combination with micro and macro-level behavior. Therefore, this study incorporates a multi-scale approach using computational modeling and laboratory experiments to provide an in-depth understanding of the mechanisms of interaction between selected modifiers and the constituents of asphalt binder, at aged and unaged conditions. This study investigated the effect of paraffinic wax as a modifier for virgin binder in warm-mix asphalt that can reduce the environmental burden of asphalt pavements. The addition of wax was shown to reduce the viscosity of bitumen by reducing the self-interaction of asphaltene molecules and penetrating the existing nano agglomerates of asphaltenes. This study further examined how the interplay of various modifiers affects the modified binder’s thermomechanical properties. It was found that the presence of wax-based modifiers has a disrupting effect on the role of polyphosphoric acid that is another modifier of bitumen and its interactions with resin-type molecules.

This study was further extended to using nanozeolite as a mineral carrier for wax to better disperse wax in bitumen and reduce the wax's adverse effects such as physical hardening at low service temperatures and rutting at high service temperatures. This novel technique showed that using a different method of adding a modifier can help reduce the modifier's unwanted effects. It further showed that nanozeolite could carry wax-based modifiers and release them in bitumen, then acting as a scavenger for acidic compounds in the binder. This, in turn, could promote the resistance of asphalt binder to moisture damage by reducing the quantity of acidic compounds at the interface between the binder and the stone aggregates.

Furthermore, this study shows that iso-paraffin wax can reduce oxidized asphaltene molecules self-interaction and therefore, reduce the viscosity of aged bitumen while cause brittleness at low temperatures.

Additionally, a cradle to gate life-cycle assessment was performed for a new bio-modifier obtained from swine manure. This study showed that by partially replacing the bitumen with bio-binder from swine manure, the carbon footprint of the binder can be reduced by 10% in conjunction with reducing the cost and environmental impact of storing the manure in lagoons.

ContributorsSamieadel, Alireza (Author) / Fini, Elham H (Thesis advisor) / Kaloush, Kamil (Committee member) / Parrish, Kristen (Committee member) / Sharma, Brajendra Kumar (Committee member) / Parast, Mahour M (Committee member) / Arizona State University (Publisher)

Created2020