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.
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- Creators: Mobasher, Barzin
Description
Concrete is relatively brittle, and its tensile strength is typically only about one-tenth of its compressive strength. Regular concrete is therefore normally uses reinforcement steel bars to increase the tensile strength. It is becoming increasingly popular to use random distributed fibers as reinforcement and polymeric fibers is once such kind. In the case of polymeric fibers, due to hydrophobicity and lack of any chemical bond between the fiber and matrix, the weak interface zone limits the ability of the fibers to effectively carry the load that is on the matrix phase. Depending on the fiber’s surface asperity, shape, chemical nature, and mechanical bond characteristic of the load transfer between matrix and fiber can be altered so that the final composite can be improved. These modifications can be carried out by means of thermal treatment, mechanical surface modifications, or chemical changes The objective of this study is to measure and document the effect of gamma ray irradiation on the mechanical properties of macro polymeric fibers. The objective is to determine the mechanical properties of macro-synthetic fibers and develop guidelines for treatment and characterization that allow for potential positive changes due to exposure to irradiation. Fibers are exposed to various levels of ionizing radiation and the tensile, interface and performance in a mortar matrix are documented. Uniaxial tensile tests were performed on irradiated fibers to study fiber strength and failure pattern. SEM tests were carried out in order to study the surface characteristic and effect of different radiation dose on polymeric fiber. The interaction of the irradiated fiber with the cement composite was studied by a series of quasi-static pullout test for a specific embedded length. As a final task, flexural tests were carried out for different irradiated fibers to sum up the investigation. An average increase of 13% in the stiffness of the fiber was observed for 5 kGy of radiation. Flexural tests showed an average increase of 181% in the Req3 value and 102 % in the toughness of the sample was observed for 5 kGy of dose.
ContributorsTiwari, Sanchay Sushil (Author) / Mobasher, Barzin (Thesis advisor) / Neithalath, Narayanan (Thesis advisor) / Dharmarajan, Subramaniam (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Ultra High Performance (UHP) cementitious binders are a class of cement-based materials with high strength and ductility, designed for use in precast bridge connections, bridge superstructures, high load-bearing structural members like columns, and in structural repair and strengthening. This dissertation aims to elucidate the chemo-mechanical relationships in complex UHP binders to facilitate better microstructure-based design of these materials and develop machine learning (ML) models to predict their scale-relevant properties from microstructural information.To establish the connection between micromechanical properties and constitutive materials, nanoindentation and scanning electron microscopy experiments are performed on several cementitious pastes. Following Bayesian statistical clustering, mixed reaction products with scattered nanomechanical properties are observed, attributable to the low degree of reaction of the constituent particles, enhanced particle packing, and very low water-to-binder ratio of UHP binders. Relating the phase chemistry to the micromechanical properties, the chemical intensity ratios of Ca/Si and Al/Si are found to be important parameters influencing the incorporation of Al into the C-S-H gel.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ContributorsFord, Emily Lucile (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Hoover, Christian G. (Committee member) / Maneparambil, Kailas (Committee member) / Arizona State University (Publisher)
Created2020