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.

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Description
With the increasing focus on developing environmentally benign electronic packages, lead-free solder alloys have received a great deal of attention. Mishandling of packages, during manufacture, assembly, or by the user may cause failure of solder joint. A fundamental understanding of the behavior of lead-free solders under mechanical shock conditions is

With the increasing focus on developing environmentally benign electronic packages, lead-free solder alloys have received a great deal of attention. Mishandling of packages, during manufacture, assembly, or by the user may cause failure of solder joint. A fundamental understanding of the behavior of lead-free solders under mechanical shock conditions is lacking. Reliable experimental and numerical analysis of lead-free solder joints in the intermediate strain rate regime need to be investigated. This dissertation mainly focuses on exploring the mechanical shock behavior of lead-free tin-rich solder alloys via multiscale modeling and numerical simulations. First, the macroscopic stress/strain behaviors of three bulk lead-free tin-rich solders were tested over a range of strain rates from 0.001/s to 30/s. Finite element analysis was conducted to determine appropriate specimen geometry that could reach a homogeneous stress/strain field and a relatively high strain rate. A novel self-consistent true stress correction method is developed to compensate the inaccuracy caused by the triaxial stress state at the post-necking stage. Then the material property of micron-scale intermetallic was examined by micro-compression test. The accuracy of this measure is systematically validated by finite element analysis, and empirical adjustments are provided. Moreover, the interfacial property of the solder/intermetallic interface is investigated, and a continuum traction-separation law of this interface is developed from an atomistic-based cohesive element method. The macroscopic stress/strain relation and microstructural properties are combined together to form a multiscale material behavior via a stochastic approach for both solder and intermetallic. As a result, solder is modeled by porous plasticity with random voids, and intermetallic is characterized as brittle material with random vulnerable region. Thereafter, the porous plasticity fracture of the solders and the brittle fracture of the intermetallics are coupled together in one finite element model. Finally, this study yields a multiscale model to understand and predict the mechanical shock behavior of lead-free tin-rich solder joints. Different fracture patterns are observed for various strain rates and/or intermetallic thicknesses. The predictions have a good agreement with the theory and experiments.
ContributorsFei, Huiyang (Author) / Jiang, Hanqing (Thesis advisor) / Chawla, Nikhilesh (Thesis advisor) / Tasooji, Amaneh (Committee member) / Mobasher, Barzin (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Concrete columns constitute the fundamental supports of buildings, bridges, and various other infrastructures, and their failure could lead to the collapse of the entire structure. As such, great effort goes into improving the fire resistance of such columns. In a time sensitive fire situation, a delay in the failure of

Concrete columns constitute the fundamental supports of buildings, bridges, and various other infrastructures, and their failure could lead to the collapse of the entire structure. As such, great effort goes into improving the fire resistance of such columns. In a time sensitive fire situation, a delay in the failure of critical load bearing structures can lead to an increase in time allowed for the evacuation of occupants, recovery of property, and access to the fire. Much work has been done in improving the structural performance of concrete including reducing column sizes and providing a safer structure. As a result, high-strength (HS) concrete has been developed to fulfill the needs of such improvements. HS concrete varies from normal-strength (NS) concrete in that it has a higher stiffness, lower permeability and larger durability. This, unfortunately, has resulted in poor performance under fire. The lower permeability allows for water vapor to build up causing HS concrete to suffer from explosive spalling under rapid heating. In addition, the coefficient of thermal expansion (CTE) of HS concrete is lower than that of NS concrete. In this study, the effects of introducing a region of crumb rubber concrete into a steel-reinforced concrete column were analyzed. The inclusion of crumb rubber concrete into a column will greatly increase the thermal resistivity of the overall column, leading to a reduction in core temperature as well as the rate at which the column is heated. Different cases were analyzed while varying the positioning of the crumb-rubber region to characterize the effect of position on the improvement of fire resistance. Computer simulated finite element analysis was used to calculate the temperature and strain distribution with time across the column's cross-sectional area with specific interest in the steel - concrete region. Of the several cases which were investigated, it was found that the improvement of time before failure ranged between 32 to 45 minutes.
ContributorsZiadeh, Bassam Mohammed (Author) / Phelan, Patrick (Thesis advisor) / Kaloush, Kamil (Thesis advisor) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2011
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Description

This study presents the results of one of the first attempts to characterize the pore water pressure response of soils subjected to traffic loading under saturated and unsaturated conditions. It is widely known that pore water pressure develops within the soil pores as a response to external stimulus. Also, it

This study presents the results of one of the first attempts to characterize the pore water pressure response of soils subjected to traffic loading under saturated and unsaturated conditions. It is widely known that pore water pressure develops within the soil pores as a response to external stimulus. Also, it has been recognized that the development of pores water pressure contributes to the degradation of the resilient modulus of unbound materials. In the last decades several efforts have been directed to model the effect of air and water pore pressures upon resilient modulus. However, none of them consider dynamic variations in pressures but rather are based on equilibrium values corresponding to initial conditions. The measurement of this response is challenging especially in soils under unsaturated conditions. Models are needed not only to overcome testing limitations but also to understand the dynamic behavior of internal pore pressures that under critical conditions may even lead to failure. A testing program was conducted to characterize the pore water pressure response of a low plasticity fine clayey sand subjected to dynamic loading. The bulk stress, initial matric suction and dwelling time parameters were controlled and their effects were analyzed. The results were used to attempt models capable of predicting the accumulated excess pore pressure at any given time during the traffic loading and unloading phases. Important findings regarding the influence of the controlled variables challenge common beliefs. The accumulated excess pore water pressure was found to be higher for unsaturated soil specimens than for saturated soil specimens. The maximum pore water pressure always increased when the high bulk stress level was applied. Higher dwelling time was found to decelerate the accumulation of pore water pressure. In addition, it was found that the higher the dwelling time, the lower the maximum pore water pressure. It was concluded that upon further research, the proposed models may become a powerful tool not only to overcome testing limitations but also to enhance current design practices and to prevent soil failure due to excessive development of pore water pressure.

ContributorsCary, Carlos (Author) / Zapata, Claudia E (Thesis advisor) / Wiczak, Matthew W (Thesis advisor) / Kaloush, Kamil (Committee member) / Sandra, Houston (Committee member) / Arizona State University (Publisher)
Created2011
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Description

A recent joint study by Arizona State University and the Arizona Department of Transportation (ADOT) was conducted to evaluate certain Warm Mix Asphalt (WMA) properties in the laboratory. WMA material was taken from an actual ADOT project that involved two WMA sections. The first section used a foamed-based WMA admixture,

A recent joint study by Arizona State University and the Arizona Department of Transportation (ADOT) was conducted to evaluate certain Warm Mix Asphalt (WMA) properties in the laboratory. WMA material was taken from an actual ADOT project that involved two WMA sections. The first section used a foamed-based WMA admixture, and the second section used a chemical-based WMA admixture. The rest of the project included control hot mix asphalt (HMA) mixture. The evaluation included testing of field-core specimens and laboratory compacted specimens. The laboratory specimens were compacted at two different temperatures; 270 °F (132 °C) and 310 °F (154 °C). The experimental plan included four laboratory tests: the dynamic modulus (E*), indirect tensile strength (IDT), moisture damage evaluation using AASHTO T-283 test, and the Hamburg Wheel-track Test. The dynamic modulus E* results of the field cores at 70 °F showed similar E* values for control HMA and foaming-based WMA mixtures; the E* values of the chemical-based WMA mixture were relatively higher. IDT test results of the field cores had comparable finding as the E* results. For the laboratory compacted specimens, both E* and IDT results indicated that decreasing the compaction temperatures from 310 °F to 270 °F did not have any negative effect on the material strength for both WMA mixtures; while the control HMA strength was affected to some extent. It was noticed that E* and IDT results of the chemical-based WMA field cores were high; however, the laboratory compacted specimens results didn't show the same tendency. The moisture sensitivity findings from TSR test disagreed with those of Hamburg test; while TSR results indicated relatively low values of about 60% for all three mixtures, Hamburg test results were quite excellent. In general, the results of this study indicated that both WMA mixes can be best evaluated through field compacted mixes/cores; the results of the laboratory compacted specimens were helpful to a certain extent. The dynamic moduli for the field-core specimens were higher than for those compacted in the laboratory. The moisture damage findings indicated that more investigations are needed to evaluate moisture damage susceptibility in field.

ContributorsAlossta, Abdulaziz (Author) / Kaloush, Kamil (Thesis advisor) / Witczak, Matthew W. (Committee member) / Mamlouk, Michael S. (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Increasing demand for high strength powder metallurgy (PM) steels has resulted in the development of dual phase PM steels. In this work, the effects of thermal aging on the microstructure and mechanical behavior of dual phase precipitation hardened powder metallurgy (PM) stainless steels of varying ferrite-martensite content were examined. Quantitative

Increasing demand for high strength powder metallurgy (PM) steels has resulted in the development of dual phase PM steels. In this work, the effects of thermal aging on the microstructure and mechanical behavior of dual phase precipitation hardened powder metallurgy (PM) stainless steels of varying ferrite-martensite content were examined. Quantitative analyses of the inherent porosity and phase fractions were conducted on the steels and no significant differences were noted with respect to aging temperature. Tensile strength, yield strength, and elongation to fracture all increased with increasing aging temperature reaching maxima at 538oC in most cases. Increased strength and decreased ductility were observed in steels of higher martensite content. Nanoindentation of the individual microconstituents was employed to obtain a fundamental understanding of the strengthening contributions. Both the ferrite and martensite hardness values increased with aging temperature and exhibited similar maxima to the bulk tensile properties. Due to the complex non-uniform stresses and strains associated with conventional nanoindentation, micropillar compression has become an attractive method to probe local mechanical behavior while limiting strain gradients and contributions from surrounding features. In this study, micropillars of ferrite and martensite were fabricated by focused ion beam (FIB) milling of dual phase precipitation hardened powder metallurgy (PM) stainless steels. Compression testing was conducted using a nanoindenter equipped with a flat punch indenter. The stress-strain curves of the individual microconstituents were calculated from the load-displacement curves less the extraneous displacements of the system. Using a rule of mixtures approach in conjunction with porosity corrections, the mechanical properties of ferrite and martensite were combined for comparison to tensile tests of the bulk material, and reasonable agreement was found for the ultimate tensile strength. Micropillar compression experiments of both as sintered and thermally aged material allowed for investigation of the effect of thermal aging.
ContributorsStewart, Jennifer (Author) / Chawla, Nikhilesh (Thesis advisor) / Jiang, Hanqing (Committee member) / Krause, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of

Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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Description
The structural design of pavements in both highways and airfields becomes complex when one considers environmental effects and ground water table variation. Environmental effects have been incorporated on the new Mechanistic-Empirical Pavement Design Guide (MEPDG) but little has been done to incorporate environmental effects on airfield design. This work presents

The structural design of pavements in both highways and airfields becomes complex when one considers environmental effects and ground water table variation. Environmental effects have been incorporated on the new Mechanistic-Empirical Pavement Design Guide (MEPDG) but little has been done to incorporate environmental effects on airfield design. This work presents a developed code produced from this research study called ZAPRAM, which is a mechanistically based pavement model based upon Limiting Strain Criteria in airfield HMA pavement design procedures. ZAPRAM is capable of pavement and airfield design analyses considering environmental effects. The program has been coded in Visual Basic and implemented in an event-driven, user-friendly educational computer program, which runs in Excel environment. Several studies were conducted in order to insure the validity of the analysis as well as the efficiency of the software. The first study yielded the minimum threshold number of computational points the user should use at a specific depth within the pavement system. The second study was completed to verify the correction factor for the Odemark's transformed thickness equation. Default correction factors were included in the code base on a large comparative study between Odemark's and MLET. A third study was conducted to provide a comparison of flexible airfield pavement design thicknesses derived from three widely accepted design procedures used in practice today: the Asphalt Institute, Shell Oil, and the revised Corps of Engineering rutting failure criteria to calculate the thickness requirements necessary for a range of design input variables. The results of the comparative study showed that there is a significant difference between the pavement thicknesses obtained from the three design procedures, with the greatest deviation found between the Shell Oil approach and the other two criteria. Finally, a comprehensive sensitivity study of environmental site factors and the groundwater table depth upon flexible airfield pavement design and performance was completed. The study used the newly revised USACE failure criteria for subgrade shear deformation. The methodology utilized the same analytical methodology to achieve real time environmental effects upon unbound layer modulus, as that used in the new AASHTO MEPDG. The results of this effort showed, for the first time, the quantitative impact of the significant effects of the climatic conditions at the design site, coupled with the importance of the depth of the groundwater table, on the predicted design thicknesses. Significant cost savings appear to be quite reasonable by utilizing principles of unsaturated soil mechanics into the new airfield pavement design procedure found in program ZAPRAM.
ContributorsSalim, Ramadan A (Author) / Zapata, Claudia (Thesis advisor) / Witczak, Matthew (Thesis advisor) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It

Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
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Description

In recent years, an increase of environmental temperature in urban areas has raised many concerns. These areas are subjected to higher temperature compared to the rural surrounding areas. Modification of land surface and the use of materials such as concrete and/or asphalt are the main factors influencing the surface energy

In recent years, an increase of environmental temperature in urban areas has raised many concerns. These areas are subjected to higher temperature compared to the rural surrounding areas. Modification of land surface and the use of materials such as concrete and/or asphalt are the main factors influencing the surface energy balance and therefore the environmental temperature in the urban areas. Engineered materials have relatively higher solar energy absorption and tend to trap a relatively higher incoming solar radiation. They also possess a higher heat storage capacity that allows them to retain heat during the day and then slowly release it back into the atmosphere as the sun goes down. This phenomenon is known as the Urban Heat Island (UHI) effect and causes an increase in the urban air temperature. Many researchers believe that albedo is the key pavement affecting the urban heat island. However, this research has shown that the problem is more complex and that solar reflectivity may not be the only important factor to evaluate the ability of a pavement to mitigate UHI. The main objective of this study was to analyze and research the influence of pavement materials on the near surface air temperature. In order to accomplish this effort, test sections consisting of Hot Mix Asphalt (HMA), Porous Hot Mix asphalt (PHMA), Portland Cement Concrete (PCC), Pervious Portland Cement Concrete (PPCC), artificial turf, and landscape gravels were constructed in the Phoenix, Arizona area. Air temperature, albedo, wind speed, solar radiation, and wind direction were recorded, analyzed and compared above each pavement material type. The results showed that there was no significant difference in the air temperature at 3-feet and above, regardless of the type of the pavement. Near surface pavement temperatures were also measured and modeled. The results indicated that for the UHI analysis, it is important to consider the interaction between pavement structure, material properties, and environmental factors. Overall, this study demonstrated the complexity of evaluating pavement structures for UHI mitigation; it provided great insight on the effects of material types and properties on surface temperatures and near surface air temperature.

ContributorsPourshams-Manzouri, Tina (Author) / Kaloush, Kamil (Thesis advisor) / Wang, Zhihua (Thesis advisor) / Zapata, Claudia E. (Committee member) / Mamlouk, Michael (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups

Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013