Document Type

Article

Journal/Book Title/Conference

International Journal of Concrete Structures and Materials

Volume

13

Issue

20

Publisher

Springer Singapore

Publication Date

3-6-2019

First Page

1

Last Page

18

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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, 螖饾憮饾憹饾憼, 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 螖饾憮饾憹饾憼 are made via Principal Component Regression models, and the method proposed, linear models using SPCA, are shown to improve over current models (best case 饾憛2 of 0.27, measured-to-predicted ratio [位] of 1.34) with linear equations. These models produced an 饾憛2 of 0.54, 0.70 and 位 of 1.03, and 0.99 for the internal and external datasets respectively.

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