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Probabilistic Capacity Model for Concrete-Filled Steel Tubes

 Probabilistic Capacity Model for Concrete-Filled Steel Tubes
Author(s): , , , ,
Presented at IABSE Symposium: Long Span Bridges, Istanbul, Turkey, 26-28 April 2023, published in , pp. 269-276
DOI: 10.2749/istanbul.2023.0269
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Concrete-filled steel tubular (CFST) columns are increasingly used around the world due to their significant structural and economic advantages. Although considerable research and several experimen...
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Bibliographic Details

Author(s): (College of Civil Engineering, Fuzhou University Fuzhou, China)
(Department of Civil, Construction-Architecture and Environmental Engineering, University of L’Aquila L’Aquila, Italy)
(College of Civil Engineering, Fuzhou University Fuzhou, China)
(Department of Structural and Geotechnical Engineering, Sapienza University of Rome Rome, Italy)
(Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, USA)
(College of Civil Engineering, Fuzhou University Fuzhou, China)
Medium: conference paper
Language(s): English
Conference: IABSE Symposium: Long Span Bridges, Istanbul, Turkey, 26-28 April 2023
Published in:
Page(s): 269-276 Total no. of pages: 8
Page(s): 269-276
Total no. of pages: 8
Year: 2023
DOI: 10.2749/istanbul.2023.0269
Abstract:

Concrete-filled steel tubular (CFST) columns are increasingly used around the world due to their significant structural and economic advantages. Although considerable research and several experimental tests have been carried out on CFST columns, there are no mechanics-based probabilistic models of their axial capacity. The present research proposes a mechanicsbased probabilistic capacity model for the assessment of the ultimate axial capacity of CFST columns. The accuracy of the numerical predictions obtained with the proposed formulation is compared with that of existing capacity equations already in use within technical standards or available in the literature.

Keywords:
axial capacity concrete-filled steel tubes probabilistic capacity model