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Theoretical prediction on the shear capacities of shear connectors in steel column bases

Theoretical prediction on the shear capacities of shear connectors in steel column bases

Xiao, Nan and Chen, Hua Peng (2011) Theoretical prediction on the shear capacities of shear connectors in steel column bases. Advances in Structural Engineering, 14 (5). pp. 953-966. ISSN 1369-4332 (doi:10.1260/1369-4332.14.5.953)

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Abstract

Shear connectors are extensively used in steel column bases of steel structures to transfer loading from superstructure to concrete foundations, however the behavior and shear capacity of the steel shear connector subject to complex external loading are not well understood. This paper presents an approach for theoretically predicting the shear capacities as well as behavior of the shear connector in steel column bases subject to external loading such as both shear force and bending moment at the top. The load-displacement behavior at the top of the shear connector and the internal force distribution along the depth of the connector are also investigated at various states with different cases of boundary conditions. Two states of concrete compressive strain, i.e. the critical and ultimate states, are considered for evaluating the critical and ultimate shear capacities of the shear connector. The theoretical predictions for the behavior and load capacities of the shear connector are then compared with finite element results. The results from the verification study show that the proposed approach can provide satisfactory theoretical predictions for the behavior of the shear connector

Item Type: Article
Uncontrolled Keywords: steel column base, shear connector, load-displacement relationship, shear capacities, Timoshenko beam theory, theoretical prediction
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Pre-2014 Departments: School of Engineering
School of Engineering > Department of Civil Engineering
Related URLs:
Last Modified: 14 Oct 2016 09:18
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/7395

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