Title: Prediction of Stress Increase at Ultimate in Unbonded Tendons Using Sparse Principal Component Analysis

Author(s): Eric McKinney, Minwoo Chang, Marc Maguire and Yan Sun

Publication: IJCSM

Volume: 13

Issue:

Appears on pages(s):

Keywords: Principal Component Analysis, Sparse Principal Component Analysis, unbonded tendons, strand stress increase, LASSO

DOI: 10.1186/s40069-019-0339-y

Date: 3/31/2019

Abstract:
While internal and external unbonded tendons are widely utilized in concrete structures, an analytical solution for the increase in unbonded tendon stress at ultimate strength, fps , 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. The aim of this paper is to use advanced statistical techniques to develop a solution to the unbonded strand stress increase problem, which phenomenological models by themselves have done poorly. In this paper, 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 a database of Continuous unbonded tendon reinforced members in the literature. Predictions of fps are made via Principal Component Regression models, and the method proposed, linear models using SPCA, are shown to improve over current models (best case R2 of 0.27, measured-topredicted ratio [λ] of 1.34) with linear equations. These models produced an R2 of 0.54, 0.70 and λ of 1.03, and 0.99 for the internal and external datasets respectively.