Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Arora, Aashay
- Creators: Turaga, Pavan
Description
Being a remarkably versatile and inexpensive building material, concrete has found tremendous use in development of modern infrastructure and is the most widely used material in the world. Extensive research in the field of concrete has led to the development of a wide array of concretes with applications ranging from building of skyscrapers to paving of highways. These varied applications require special cementitious composites which can satisfy the demand for enhanced functionalities such as high strength, high durability and improved thermal characteristics among others.
The current study focuses on the fundamental understanding of such functional composites, from their microstructural design to macro-scale application. More specifically, this study investigates three different categories of functional cementitious composites. First, it discusses the differences between cementitious systems containing interground and blended limestone with and without alumina. The interground systems are found to outperform the blended systems due to differential grinding of limestone. A novel approach to deduce the particle size distribution of limestone and cement in the interground systems is proposed. Secondly, the study delves into the realm of ultra-high performance concrete, a novel material which possesses extremely high compressive-, tensile- and flexural-strength and service life as compared to regular concrete. The study presents a novel first principles-based paradigm to design economical ultra-high performance concretes using locally available materials. In the final part, the study addresses the thermal benefits of a novel type of concrete containing phase change materials. A software package was designed to perform numerical simulations to analyze temperature profiles and thermal stresses in concrete structures containing PCMs.
The design of these materials is accompanied by material characterization of cementitious binders. This has been accomplished using techniques that involve measurement of heat evolution (isothermal calorimetry), determination and quantification of reaction products (thermo-gravimetric analysis, x-ray diffraction, micro-indentation, scanning electron microscopy, energy-dispersive x-ray spectroscopy) and evaluation of pore-size distribution (mercury intrusion porosimetry). In addition, macro-scale testing has been carried out to determine compression, flexure and durability response. Numerical simulations have been carried out to understand hydration of cementitious composites, determine optimum particle packing and determine the thermal performance of these composites.
The current study focuses on the fundamental understanding of such functional composites, from their microstructural design to macro-scale application. More specifically, this study investigates three different categories of functional cementitious composites. First, it discusses the differences between cementitious systems containing interground and blended limestone with and without alumina. The interground systems are found to outperform the blended systems due to differential grinding of limestone. A novel approach to deduce the particle size distribution of limestone and cement in the interground systems is proposed. Secondly, the study delves into the realm of ultra-high performance concrete, a novel material which possesses extremely high compressive-, tensile- and flexural-strength and service life as compared to regular concrete. The study presents a novel first principles-based paradigm to design economical ultra-high performance concretes using locally available materials. In the final part, the study addresses the thermal benefits of a novel type of concrete containing phase change materials. A software package was designed to perform numerical simulations to analyze temperature profiles and thermal stresses in concrete structures containing PCMs.
The design of these materials is accompanied by material characterization of cementitious binders. This has been accomplished using techniques that involve measurement of heat evolution (isothermal calorimetry), determination and quantification of reaction products (thermo-gravimetric analysis, x-ray diffraction, micro-indentation, scanning electron microscopy, energy-dispersive x-ray spectroscopy) and evaluation of pore-size distribution (mercury intrusion porosimetry). In addition, macro-scale testing has been carried out to determine compression, flexure and durability response. Numerical simulations have been carried out to understand hydration of cementitious composites, determine optimum particle packing and determine the thermal performance of these composites.
ContributorsArora, Aashay (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Hoover, Christian G (Committee member) / Arizona State University (Publisher)
Created2018
Description
Semantic image segmentation has been a key topic in applications involving image processing and computer vision. Owing to the success and continuous research in the field of deep learning, there have been plenty of deep learning-based segmentation architectures that have been designed for various tasks. In this thesis, deep-learning architectures for a specific application in material science; namely the segmentation process for the non-destructive study of the microstructure of Aluminum Alloy AA 7075 have been developed. This process requires the use of various imaging tools and methodologies to obtain the ground-truth information. The image dataset obtained using Transmission X-ray microscopy (TXM) consists of raw 2D image specimens captured from the projections at every beam scan. The segmented 2D ground-truth images are obtained by applying reconstruction and filtering algorithms before using a scientific visualization tool for segmentation. These images represent the corrosive behavior caused by the precipitates and inclusions particles on the Aluminum AA 7075 alloy. The study of the tools that work best for X-ray microscopy-based imaging is still in its early stages.
In this thesis, the underlying concepts behind Convolutional Neural Networks (CNNs) and state-of-the-art Semantic Segmentation architectures have been discussed in detail. The data generation and pre-processing process applied to the AA 7075 Data have also been described, along with the experimentation methodologies performed on the baseline and four other state-of-the-art Segmentation architectures that predict the segmented boundaries from the raw 2D images. A performance analysis based on various factors to decide the best techniques and tools to apply Semantic image segmentation for X-ray microscopy-based imaging was also conducted.
In this thesis, the underlying concepts behind Convolutional Neural Networks (CNNs) and state-of-the-art Semantic Segmentation architectures have been discussed in detail. The data generation and pre-processing process applied to the AA 7075 Data have also been described, along with the experimentation methodologies performed on the baseline and four other state-of-the-art Segmentation architectures that predict the segmented boundaries from the raw 2D images. A performance analysis based on various factors to decide the best techniques and tools to apply Semantic image segmentation for X-ray microscopy-based imaging was also conducted.
ContributorsBarboza, Daniel (Author) / Turaga, Pavan (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2020