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: Stone, David
- Creators: Turaga, Pavan
Description
Increased priority on the minimization of environmental impacts of conventional construction materials in recent years has motivated increased use of waste materials or bi-products such as fly ash, blast furnace slag with a view to reduce or eliminate the manufacturing/consumption of ordinary portland cement (OPC) which accounts for approximately 5-7% of global carbon dioxide emission. The current study explores, for the first time, the possibility of carbonating waste metallic iron powder to develop carbon-negative sustainable binder systems for concrete. The fundamental premise of this work is that metallic iron will react with aqueous CO2 under controlled conditions to form complex iron carbonates which have binding capabilities. The compressive and flexural strengths of the chosen iron-based binder systems increase with carbonation duration and the specimens carbonated for 4 days exhibit mechanical properties that are comparable to those of companion ordinary portland cement systems. The optimal mixture proportion and carbonation regime for this non-conventional sustainable binder is established based on the study of carbonation efficiency of a series of mixtures using thermogravimetric analysis. The pore- and micro-structural features of this novel binding material are also evaluated. The fracture response of this novel binder is evaluated using strain energy release rate and measurement of fracture process zone using digital image correlation (DIC). The iron-based binder system exhibits significantly higher strain energy release rates when compared to those of the OPC systems in both the unreinforced and glass fiber reinforced states. The iron-based binder also exhibits higher amount of area of fracture process zone due to its ability to undergo inelastic deformation facilitated by unreacted metallic iron particle inclusions in the microstructure that helps crack bridging /deflection. The intrinsic nano-mechanical properties of carbonate reaction product are explored using statistical nanoindentation technique coupled with a stochastic deconvolution algorithm. Effect of exposure to high temperature (up to 800°C) is also studied. Iron-based binder shows significantly higher residual flexural strength after exposure to high temperatures. Results of this comprehensive study establish the viability of this binder type for concrete as an environment-friendly and economical alternative to OPC.
ContributorsDas, Sumanta (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, S.D. (Committee member) / Mobasher, Barzin (Committee member) / Marzke, Robert (Committee member) / Chawla, Nikhilesh (Committee member) / Stone, David (Committee member) / Arizona State University (Publisher)
Created2015
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