Matching Items (2)
Filtering by
- Genre: Academic theses
Description
I believe the human mind is not an accurate reproducer of objects and events, but a tool that constructs their qualities. Philosophers Bowman Clarke, James John, and Amy Kind have argued for and against similar points, while Daniel Hoffman and Jay Dowling have debated cases from a psychological perspective. My understanding of their discourse surfaces in Cognize Normal-Like Pleez, a video installation designed to capture the enigmatic connection between perceivers and the things they perceive. The composition encapsulates this theme through a series of five videos that disseminate confusing imagery paired with mangled sounds. The miniatures operate in sequence on computer monitors set inside a haphazardously ornamented tower. Though the original sources for each video communicate clear, familiar subjects, the final product deliberately obscures them. Sometimes sounds and images flicker for only brief moments, perhaps too fast for the human mind to fully process. Though some information comes through, important data supplied by the surrounding context is absent.
I invite the audience to rationalize this complexing conglomerate and reflect on how their established biases inform their opinion of the work. Each person likely draws from his or her experiences, cultural conditioning, knowledge, and other personal factors in order to create an individual conceptualization of the installation. Their subjective conclusions reflect my belief concerning a neurological basis for the origin of qualities. One’s connection to Cognize’s images and sounds, to me, is not derived solely from characteristics inherent to it, but also endowed by one’s mind, which not only constructs the attributes one normally associates with the images and sounds (as opposed to the physics and biology that lead to their construction), but also seamlessly incorporates the aforementioned biases. I realize my ideas by focusing the topics of the videos and their setting around the transmission of information and its obfuscation. Just as one cannot see or hear past the perceptual barriers in Cognize, I believe one cannot escape his or her mind to “sense” qualities in an objective, disembodied manner, because the mind is necessary for perception.
I invite the audience to rationalize this complexing conglomerate and reflect on how their established biases inform their opinion of the work. Each person likely draws from his or her experiences, cultural conditioning, knowledge, and other personal factors in order to create an individual conceptualization of the installation. Their subjective conclusions reflect my belief concerning a neurological basis for the origin of qualities. One’s connection to Cognize’s images and sounds, to me, is not derived solely from characteristics inherent to it, but also endowed by one’s mind, which not only constructs the attributes one normally associates with the images and sounds (as opposed to the physics and biology that lead to their construction), but also seamlessly incorporates the aforementioned biases. I realize my ideas by focusing the topics of the videos and their setting around the transmission of information and its obfuscation. Just as one cannot see or hear past the perceptual barriers in Cognize, I believe one cannot escape his or her mind to “sense” qualities in an objective, disembodied manner, because the mind is necessary for perception.
ContributorsLempke, John Paul (Author) / Suzuki, Kotoka (Thesis advisor) / Knowles, Kristina (Committee member) / Stover, Chris (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