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 - 10 of 72
Description
The effect of earthquake-induced liquefaction on the local void ratio distribution of cohesionless soil is evaluated using x-ray computed tomography (CT) and an advanced image processing software package. Intact, relatively undisturbed specimens of cohesionless soil were recovered before and after liquefaction by freezing and coring soil deposits created by pluviation and by sedimentation through water. Pluviated soil deposits were liquefied in the small geotechnical centrifuge at the University of California at Davis shared-use National Science Foundation (NSF)-supported Network for Earthquake Engineering Simulation (NEES) facility. A soil deposit created by sedimentation through water was liquefied on a small shake table in the Arizona State University geotechnical laboratory. Initial centrifuge tests employed Ottawa 20-30 sand but this material proved to be too coarse to liquefy in the centrifuge. Therefore, subsequent centrifuge tests employed Ottawa F60 sand. The shake table test employed Ottawa 20-30 sand. Recovered cores were stabilized by impregnation with optical grade epoxy and sent to the University of Texas at Austin NSF-supported facility at the University of Texas at Austin for high-resolution CT scanning of geologic media. The local void ratio distribution of a CT-scanned core of Ottawa 20-30 sand evaluated using Avizo® Fire, a commercially available advanced program for image analysis, was compared to the local void ratio distribution established on the same core by analysis of optical images to demonstrate that analysis of the CT scans gave similar results to optical methods. CT scans were subsequently conducted on liquefied and not-liquefied specimens of Ottawa 20-30 sand and Ottawa F60 sand. The resolution of F60 specimens was inadequate to establish the local void ratio distribution. Results of the analysis of the Ottawa 20-30 specimens recovered from the model built for the shake table test showed that liquefaction can substantially influence the variability in local void ratio, increasing the degree of non-homogeneity in the specimen.
ContributorsGutierrez, Angel (Author) / Kavazanjian, Edward (Thesis advisor) / Houston, Sandra (Committee member) / Zapata, Claudia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Heating of asphalt during production and construction causes the volatilization and oxidation of binders used in mixes. Volatilization and oxidation causes degradation of asphalt pavements by increasing the stiffness of the binders, increasing susceptibility to cracking and negatively affecting the functional and structural performance of the pavements. Degradation of asphalt binders by volatilization and oxidation due to high production temperature occur during early stages of pavement life and are known as Short Term Aging (STA). Elevated temperatures and increased exposure time to elevated temperatures causes increased STA of asphalt. The objective of this research was to investigate how elevated mixing temperatures and exposure time to elevated temperatures affect aging and stiffening of binders, thus influencing properties of the asphalt mixtures. The study was conducted in two stages. The first stage evaluated STA effect of asphalt binders. It involved aging two Performance Graded (PG) virgin asphalt binders, PG 76-16 and PG 64-22 at two different temperatures and durations, then measuring their viscosities. The second stage involved evaluating the effects of elevated STA temperature and time on properties of the asphalt mixtures. It involved STA of asphalt mixtures produced in the laboratory with the PG 64-22 binder at mixing temperatures elevated 25OF above standard practice; STA times at 2 and 4 hours longer than standard practices, and then compacted in a gyratory compactor. Dynamic modulus (E*) and Indirect Tensile Strength (IDT) were measured for the aged mixtures for each temperature and duration to determine the effect of different aging times and temperatures on the stiffness and fatigue properties of the aged asphalt mixtures. The binder test results showed that in all cases, there was increased viscosity. The results showed the highest increase in viscosity resulted from increased aging time. The results also indicated that PG 64-22 was more susceptible to elevated STA temperature and extended time than the PG 76-16 binders. The asphalt mixture test results confirmed the expected outcome that increasing the STA and mixing temperature by 25oF alters the stiffness of mixtures. Significant change in the dynamic modulus mostly occurred at four hour increase in STA time regardless of temperature.
ContributorsLolly, Rubben (Author) / Kaloush, Kamil (Thesis advisor) / Bearup, Wylie (Committee member) / Zapata, Claudia (Committee member) / Mamlouk, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Unsaturated soil mechanics is becoming a part of geotechnical engineering practice, particularly in applications to moisture sensitive soils such as expansive and collapsible soils and in geoenvironmental applications. The soil water characteristic curve, which describes the amount of water in a soil versus soil suction, is perhaps the most important soil property function for application of unsaturated soil mechanics. The soil water characteristic curve has been used extensively for estimating unsaturated soil properties, and a number of fitting equations for development of soil water characteristic curves from laboratory data have been proposed by researchers. Although not always mentioned, the underlying assumption of soil water characteristic curve fitting equations is that the soil is sufficiently stiff so that there is no change in total volume of the soil while measuring the soil water characteristic curve in the laboratory, and researchers rarely take volume change of soils into account when generating or using the soil water characteristic curve. Further, there has been little attention to the applied net normal stress during laboratory soil water characteristic curve measurement, and often zero to only token net normal stress is applied. The applied net normal stress also affects the volume change of the specimen during soil suction change. When a soil changes volume in response to suction change, failure to consider the volume change of the soil leads to errors in the estimated air-entry value and the slope of the soil water characteristic curve between the air-entry value and the residual moisture state. Inaccuracies in the soil water characteristic curve may lead to inaccuracies in estimated soil property functions such as unsaturated hydraulic conductivity. A number of researchers have recently recognized the importance of considering soil volume change in soil water characteristic curves. The study of correct methods of soil water characteristic curve measurement and determination considering soil volume change, and impacts on the unsaturated hydraulic conductivity function was of the primary focus of this study. Emphasis was placed upon study of the effect of volume change consideration on soil water characteristic curves, for expansive clays and other high volume change soils. The research involved extensive literature review and laboratory soil water characteristic curve testing on expansive soils. The effect of the initial state of the specimen (i.e. slurry versus compacted) on soil water characteristic curves, with regard to volume change effects, and effect of net normal stress on volume change for determination of these curves, was studied for expansive clays. Hysteresis effects were included in laboratory measurements of soil water characteristic curves as both wetting and drying paths were used. Impacts of soil water characteristic curve volume change considerations on fluid flow computations and associated suction-change induced soil deformations were studied through numerical simulations. The study includes both coupled and uncoupled flow and stress-deformation analyses, demonstrating that the impact of volume change consideration on the soil water characteristic curve and the estimated unsaturated hydraulic conductivity function can be quite substantial for high volume change soils.
ContributorsBani Hashem, Elham (Author) / Houston, Sandra L. (Thesis advisor) / Kavazanjian, Edward (Committee member) / Zapata, Claudia (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The research is divided into three parts. The first part addresses the problem of asymmetric loss. A proposed asymmetric support vector machine (aSVM) is used to predict specific classes with high accuracy. aSVM was shown to produce higher precision than a regular SVM. The second part addresses asymmetric data sets where variables are only predictive for a subset of the predictor classes. Asymmetric Random Forest (ARF) was proposed to detect these kinds of variables. The third part explores variable selection for matched data sets. Matched Random Forest (MRF) was proposed to find variables that are able to distinguish case and control without the restrictions that exists in linear models. MRF detects variables that are able to distinguish case and control even in the presence of interaction and qualitative variables.
ContributorsKoh, Derek (Author) / Runger, George C. (Thesis advisor) / Wu, Tong (Committee member) / Pan, Rong (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis presents a probabilistic evaluation of multiple laterally loaded drilled pier foundation design approaches using extensive data from a geotechnical investigation for a high voltage electric transmission line. A series of Monte Carlo simulations provide insight about the computed level of reliability considering site standard penetration test blow count value variability alone (i.e., assuming all other aspects of the design problem do not contribute error or bias). Evaluated methods include Eurocode 7 Geotechnical Design procedures, the Federal Highway Administration drilled shaft LRFD design method, the Electric Power Research Institute transmission foundation design procedure and a site specific variability based approach previously suggested by the author of this thesis and others. The analysis method is defined by three phases: a) Evaluate the spatial variability of an existing subsurface database. b) Derive theoretical foundation designs from the database in accordance with the various design methods identified. c) Conduct Monti Carlo Simulations to compute the reliability of the theoretical foundation designs. Over several decades, reliability-based foundation design (RBD) methods have been developed and implemented to varying degrees for buildings, bridges, electric systems and other structures. In recent years, an effort has been made by researchers, professional societies and other standard-developing organizations to publish design guidelines, manuals and standards concerning RBD for foundations. Most of these approaches rely on statistical methods for quantifying load and resistance probability distribution functions with defined reliability levels. However, each varies with regard to the influence of site-specific variability on resistance. An examination of the influence of site-specific variability is required to provide direction for incorporating the concept into practical RBD design methods. Recent surveys of transmission line engineers by the Electric Power Research Institute (EPRI) demonstrate RBD methods for the design of transmission line foundations have not been widely adopted. In the absence of a unifying design document with established reliability goals, transmission line foundations have historically performed very well, with relatively few failures. However, such a track record with no set reliability goals suggests, at least in some cases, a financial premium has likely been paid.
ContributorsHeim, Zackary (Author) / Houston, Sandra (Thesis advisor) / Witczak, Matthew (Committee member) / Kavazanjian, Edward (Committee member) / Zapata, Claudia (Committee member) / Arizona State University (Publisher)
Created2014
Description
ABSTRACT Enzyme-Induced Carbonate Precipitation (EICP) using a plant-derived form of the urease enzyme to induce the precipitation of calcium carbonate (CaCO3) shows promise as a method of stabilizing soil for the mitigation of fugitive dust. Fugitive dust is a significant problem in Arizona, particularly in Maricopa County. Maricopa County is an EPA air quality non-attainment zone, due primarily to fugitive dust, which presents a significant health risk to local residents. Conventional methods for fugitive dust control, including the application of water, are either ineffective in arid climates, very expensive, or limited to short term stabilization. Due to these limitations, engineers are searching for new and more effective ways to stabilize the soil and reduce wind erosion. EICP employs urea hydrolysis, a process in which carbonate precipitation is catalyzed by the urease enzyme, a widely occurring protein found in many plants and microorganisms. Wind tunnel experiments were conducted in the ASU/NASA Planetary Wind Tunnel to evaluate the use of EICP as a means to stabilize soil against fugitive dust emission. Three different soils were tested, including a native Arizona silty-sand, a uniform fine to medium grained silica sand, and mine tailings from a mine in southern Arizona. The test soil was loosely placed in specimen container and the surface was sprayed with an aqueous solution containing urea, calcium chloride, and urease enzyme. After a short period of time to allow for CaCO3 precipitation, the specimens were tested in the wind tunnel. The completed tests show that EICP can increase the detachment velocity compared to bare or wetted soil and thus holds promise as a means of mitigating fugitive dust emissions.
ContributorsKnorr, Brian (Author) / Kavazanjian, Edward (Thesis advisor) / Houston, Sandra (Committee member) / Zapata, Claudia (Committee member) / Arizona State University (Publisher)
Created2014
Description
Ground coupled heat pumps (GCHPs) have been used successfully in many environments to improve the heating and cooling efficiency of both small and large scale buildings. In arid climate regions, such as the Phoenix, Arizona metropolitan area, where the air condi-tioning load is dominated by cooling in the summer, GCHPs are difficult to install and operate. This is because the nature of soils in arid climate regions, in that they are both dry and hot, renders them particularly ineffective at dissipating heat.
The first part of this thesis addresses applying the SVHeat finite element modeling soft-ware to create a model of a GCHP system. Using real-world data from a prototype solar-water heating system coupled with a ground-source heat exchanger installed in Menlo Park, California, a relatively accurate model was created to represent a novel GCHP panel system installed in a shallow vertical trench. A sensitivity analysis was performed to evaluate the accuracy of the calibrated model.
The second part of the thesis involved adapting the calibrated model to represent an ap-proximation of soil conditions in arid climate regions, using a range of thermal properties for dry soils. The effectiveness of the GCHP in the arid climate region model was then evaluated by comparing the thermal flux from the panel into the subsurface profile to that of the prototype GCHP. It was shown that soils in arid climate regions are particularly inefficient at heat dissipation, but that it is highly dependent on the thermal conductivity inputted into the model. This demonstrates the importance of proper site characterization in arid climate regions. Finally, several soil improvement methods were researched to evaluate their potential for use in improving the effectiveness of shallow horizontal GCHP systems in arid climate regions.
The first part of this thesis addresses applying the SVHeat finite element modeling soft-ware to create a model of a GCHP system. Using real-world data from a prototype solar-water heating system coupled with a ground-source heat exchanger installed in Menlo Park, California, a relatively accurate model was created to represent a novel GCHP panel system installed in a shallow vertical trench. A sensitivity analysis was performed to evaluate the accuracy of the calibrated model.
The second part of the thesis involved adapting the calibrated model to represent an ap-proximation of soil conditions in arid climate regions, using a range of thermal properties for dry soils. The effectiveness of the GCHP in the arid climate region model was then evaluated by comparing the thermal flux from the panel into the subsurface profile to that of the prototype GCHP. It was shown that soils in arid climate regions are particularly inefficient at heat dissipation, but that it is highly dependent on the thermal conductivity inputted into the model. This demonstrates the importance of proper site characterization in arid climate regions. Finally, several soil improvement methods were researched to evaluate their potential for use in improving the effectiveness of shallow horizontal GCHP systems in arid climate regions.
ContributorsNorth, Timothy James (Author) / Kavazanjian, Ed (Thesis advisor) / Redy, T. Agami (Committee member) / Zapata, Claudia (Committee member) / Arizona State University (Publisher)
Created2014
Description
No-confounding designs (NC) in 16 runs for 6, 7, and 8 factors are non-regular fractional factorial designs that have been suggested as attractive alternatives to the regular minimum aberration resolution IV designs because they do not completely confound any two-factor interactions with each other. These designs allow for potential estimation of main effects and a few two-factor interactions without the need for follow-up experimentation. Analysis methods for non-regular designs is an area of ongoing research, because standard variable selection techniques such as stepwise regression may not always be the best approach. The current work investigates the use of the Dantzig selector for analyzing no-confounding designs. Through a series of examples it shows that this technique is very effective for identifying the set of active factors in no-confounding designs when there are three of four active main effects and up to two active two-factor interactions.
To evaluate the performance of Dantzig selector, a simulation study was conducted and the results based on the percentage of type II errors are analyzed. Also, another alternative for 6 factor NC design, called the Alternate No-confounding design in six factors is introduced in this study. The performance of this Alternate NC design in 6 factors is then evaluated by using Dantzig selector as an analysis method. Lastly, a section is dedicated to comparing the performance of NC-6 and Alternate NC-6 designs.
To evaluate the performance of Dantzig selector, a simulation study was conducted and the results based on the percentage of type II errors are analyzed. Also, another alternative for 6 factor NC design, called the Alternate No-confounding design in six factors is introduced in this study. The performance of this Alternate NC design in 6 factors is then evaluated by using Dantzig selector as an analysis method. Lastly, a section is dedicated to comparing the performance of NC-6 and Alternate NC-6 designs.
ContributorsKrishnamoorthy, Archana (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Thesis advisor) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2014
Description
Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
ContributorsAzarnoush, Bahareh (Author) / Runger, George C. (Thesis advisor) / Bekki, Jennifer (Thesis advisor) / Pan, Rong (Committee member) / Saghafian, Soroush (Committee member) / Arizona State University (Publisher)
Created2014