Date of Award

Summer 8-2017

Degree Type

Creative Project

Degree Name

Master of Science (MS)

Department

Mathematics and Statistics

First Advisor

Yan Sun

Abstract

While internal and external unbonded tendons are widely utilized in concrete structures, the analytic solution for the increase in unbonded tendon stress, Δ𝑓𝑝𝑠, 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 Δ𝑓𝑝𝑠 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 Δ𝑓𝑝𝑠, is shown to improve over current models without increasing complexity.

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