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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 108
PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING
Edited by: J. Kruis, Y. Tsompanakis and B.H.V. Topping
Paper 21

A Concrete Carbonation Model Factoring in the Effect of Tensile Loading

C. Jiang, X.L. Gu and W.P. Zhang

Department of Structural Engineering, Tongji University, Shanghai, P.R. China

Full Bibliographic Reference for this paper
C. Jiang, X.L. Gu, W.P. Zhang, "A Concrete Carbonation Model Factoring in the Effect of Tensile Loading", in J. Kruis, Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 21, 2015. doi:10.4203/ccp.108.21
Keywords: concrete, carbonation, tensile loading, model, performance deterioration..

Summary
Concrete in realistic structures is constantly subjected to loading as well as carbon dioxide penetration. Previous experiments have testified the accelerating effect of tensile loading on concrete carbonation. However, present carbonation models that have considered tensile loading are regressed from their respective experiments and thus are limited to the specific experimental conditions. This paper proposes a carbonation model that factors in the effect of tensile loading based on 281 measurements of carbonation depths, which inherently considers a wide range of experimental conditions. First, a modified stress level is proposed to account for stress gradients in cross sections of concrete specimens. Then, a regressed linear expression between the carbonation depth amplification factor due to tensile loading and the modified stress level is established. Subsequently, through incorporating the linear expression into the simplified carbonation model proposed by Papadakis, a carbonation model that considers the effect of tensile loading is built. Finally, through comparison between the 281 experimental carbonation depths and model predictions, the proposed model is found to be acceptable.

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