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: Chawla, Nikhilesh
Description
Weevils are among the most diverse and evolutionarily successful animal lineages on Earth. Their success is driven in part by a structure called the rostrum, which gives weevil heads a characteristic "snout-like" appearance. Nut weevils in the genus Curculio use the rostrum to drill holes into developing fruits and nuts, wherein they deposit their eggs. During oviposition this exceedingly slender structure is bent into a straightened configuration - in some species up to 90° - but does not suffer any damage during this process. The performance of the snout is explained in terms of cuticle biomechanics and rostral curvature, as presented in a series of four interconnected studies. First, a micromechanical constitutive model of the cuticle is defined to predict and reconstruct the mechanical behavior of each region in the exoskeleton. Second, the effect of increased endocuticle thickness on the stiffness and fracture strength of the rostrum is assessed using force-controlled tensile testing. In the third chapter, these studies are integrated into finite element models of the snout, demonstrating that the Curculio rostrum is only able to withstand repeated, extreme bending because of
modifications to the composite structure of the cuticle in the rostral apex. Finally, interspecific differences in the differential geometry of the snout are characterized to elucidate the role of biomechanical constraint in the evolution of rostral morphology for both males and females. Together these studies highlight the significance of cuticle biomechanics - heretofore unconsidered by others - as a source of constraint on the evolution of the rostrum and the mechanobiology of the genus Curculio.
modifications to the composite structure of the cuticle in the rostral apex. Finally, interspecific differences in the differential geometry of the snout are characterized to elucidate the role of biomechanical constraint in the evolution of rostral morphology for both males and females. Together these studies highlight the significance of cuticle biomechanics - heretofore unconsidered by others - as a source of constraint on the evolution of the rostrum and the mechanobiology of the genus Curculio.
ContributorsJansen, Michael Andrew (Author) / Franz, Nico M (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Harrison, Jon (Committee member) / Martins, Emilia (Committee member) / Arizona State University (Publisher)
Created2009
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