Date of Award
Master of Science (MS)
Mathematics and Statistics
While internal and external unbonded tendons are widely utilized in concrete structures, the analytic solution for the increase in unbonded tendon stress, Δ���, 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. . Predictions of Δ��� 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 Δ���, is shown to improve over current models without increasing complexity.
Mckinney, Eric, "Prediction of Stress Increase in Unbonded Tendons using Sparse Principal Component Analysis" (2017). All Graduate Plan B and other Reports. 1034.
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