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
The results of this work prove the feasibility of PCMs as a temperature-regulating technology. Not only do PCMs reduce and control the temperature within cementitious systems without affecting the rate of early property development but they can also be used as an auto-adaptive technology capable of improving the thermal performance of building envelopes.
Significant hardening and degradation parameters such as stiffness, crack spacing, crack width, localized zone size are obtained from tensile tests using digital image correlation (DIC) technique. A tension stiffening model is used to simulate the tensile response that addresses the cracking and localization mechanisms. The model is also modified to simulate the sequential cracking in joint-free slabs on grade reinforced by steel fibers, where the lateral stiffness of slab and grade interface and stress-crack width response are the most important model parameters.
Parametric tensile and compressive material models are used to formulate generalized analytical solutions for flexural behaviors of hybrid reinforced concrete (HRC) that contains both rebars and fibers. Design recommendations on moment capacity, minimum reinforcement ratio etc. are obtained using analytical equations. The role of fiber in reducing the amount of conventional reinforcement is revealed. The approach is extended to T-sections and used to model Ultra High Performance Concrete (UHPC) beams and girders.
The analytical models are extended to structural members subjected to combined axial and bending actions. Analytical equations to address the P-M diagrams are derived. Closed-form equations that generate the interaction diagram of HRC section are presented which may be used in the design of multiple types of applications.
The theoretical models are verified by independent experimental results from literature. Reliability analysis using Monte Carlo simulation (MCS) is conducted for few design problems on ultimate state design. The proposed methodologies enable one to simulate the experiments to obtain material parameters and design structural members using generalized formulations.
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.