Date of Award
8-2017
Degree Type
Creative Project
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
Committee Chair(s)
Yan Sun
Committee
Yan Sun
Committee
Marc Maguire
Committee
Adele Cutler
Committee
Daniel Coster
Abstract
While internal and external unbonded tendons are widely utilized in concrete structures, the analytic solution for the increase in unbonded tendon stress, ���, is challenging due to the lack of bond between strand and concrete. Moreover, most analysis methods do not provide high correlation due to the limited available test data. In this thesis, Principal Component Analysis (PCA), and Sparse Principal Component Analysis (SPCA) are employed on different sets of candidate variables, amongst the material and sectional properties from the database compiled by Maguire et al. [18]. Predictions of ��� are made via Principal Component Regression models, and the method proposed, a linear model using SPCA on variables with a significant level of correlation with ���, is shown to improve over current models without increasing complexity.
Recommended Citation
Mckinney, Eric, "Prediction of Stress Increase in Unbonded Tendons using Sparse Principal Component Analysis" (2017). All Graduate Plan B and other Reports, Spring 1920 to Spring 2023. 1034.
https://digitalcommons.usu.edu/gradreports/1034
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