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Get Free AccessUnder seismic loading, corroded reinforced concrete (RC) columns are prone to brittle shear failure, which poses a significant threat to existing structures. However, due to the mechanical defects and insufficient parameters included in the equations available in codes, the literature exhibits a lack of precision in predicting the shear strength of such columns. In this paper, a shear strength equation for the RC column was established based on the truss-arch model theory. On this basis, the effect of corrosion on key parameters such as the cross-sectional area of rebar, yield strength, compressive strength of concrete, and displacement ductility was fully considered to establish the shear strength equation of corroded RC columns. To assess the accuracy and applicability of the proposed equation, a database consisting of 215 specimen parameters was compiled. Comparative analyses were conducted with existing equations from the literature. The results indicate that the mean values and coefficient of variation for the ratio of calculated values to the tested values of the equation were 1.098 and 0.601, respectively, which proves the equation’s high computational accuracy and low dispersion. Consequently, the proposed equation offers a more effective calculation method for predicting and assessing the shear strength of corroded RC columns. This method holds significant potential for enhancing the resilience of structures in seismic-prone regions.
Jiannan Jiang, Yinhui Wang, Bo Yu, Bing Li, Jiaxing Ma (2024). A modified truss-arch model for shear strength evaluation of corroded reinforced concrete columns. , 27(9), DOI: https://doi.org/10.1177/13694332241252276.
1 dataset containing 1 research files
Jiannan Jiang, Yinhui Wang, Bo Yu, Bing Li, Jiaxing Ma. A modified truss-arch model for shear strength evaluation of corroded reinforced concrete columns. Raw data set for Databases of corroded RC columns.

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Type
Article
Year
2024
Authors
5
Datasets
1
Total Files
1
Language
en
DOI
https://doi.org/10.1177/13694332241252276
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