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 - 2 of 2
Filtering by
- All Subjects: Micromechanical
- All Subjects: structural sections
- Creators: Mobasher, Barzin
Description
Pultrusion manufacturing technique stands at the forefront for efficient production of continuous, uniform concrete composites for use in large scale structural applications. High volume and low labor, among other benefits such as improved impregnation and better sample consistency, stand as some of the crucial advances found in automated pultrusion. These advantages introduce textile reinforced concrete (TRC) composites as a potential surrogate for wood, light gauge steel, and other common structural materials into an ever changing and broadening market of industrial grade structural sections. With the potential modifications of textile geometry, textile type, section geometry, and connection type, the options presented by TRC sections seem nearly boundless. Automated pultrusion presents the ability to manufacture many different TRC composite types in at a quickened rate opening up a new field of study of structural materials.
The objective of this study centered on two studies including the development of an automated pultrusion system for the manufacturing of TRC composites and ultimately the assessment of composites created with the pultrusion technique and their viability as a relevant structural construction material. Upon planning, fabrication, and continued use of an automated pultrusion system in Arizona State University’s Structures Lab, an initial, comparative study of polypropylene microfiber composites was conducted to assess fiber reinforced concrete composites, manufactured with Filament Winding Technique, and textile reinforced concrete composites, manufactured with Automated Pultrusion Technique, in tensile and flexural mechanical response at similar reinforcement dosages. A secondary study was then conducted to measure the mechanical behavior of carbon, polypropylene, and alkali-resistant glass TRC composites and explore the response of full scale TRC structural shapes, including angle and channel sections. Finally, a study was conducted on the connection type for large scale TRC composite structural sections in tension and compression testing.
The objective of this study centered on two studies including the development of an automated pultrusion system for the manufacturing of TRC composites and ultimately the assessment of composites created with the pultrusion technique and their viability as a relevant structural construction material. Upon planning, fabrication, and continued use of an automated pultrusion system in Arizona State University’s Structures Lab, an initial, comparative study of polypropylene microfiber composites was conducted to assess fiber reinforced concrete composites, manufactured with Filament Winding Technique, and textile reinforced concrete composites, manufactured with Automated Pultrusion Technique, in tensile and flexural mechanical response at similar reinforcement dosages. A secondary study was then conducted to measure the mechanical behavior of carbon, polypropylene, and alkali-resistant glass TRC composites and explore the response of full scale TRC structural shapes, including angle and channel sections. Finally, a study was conducted on the connection type for large scale TRC composite structural sections in tension and compression testing.
ContributorsBauchmoyer, Jacob Macgregor (Author) / Mobasher, Barzin (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2017
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