Component-based model for posttensioned steel connections against progressive collapse

Yan Fei Zhu, Chang Hong Chen, Ying Huang, Zhaohui Huang, Yao Yao, Leon M. Keer

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

A component-based method for the estimation of the posttensioned (PT) steel frame against progressive collapse is proposed and presented in the current paper. A mechanical model of PT steel connections is developed and benchmarked with experimental data of a PT beam-column substructure from literature. The developd mechanical models of four PT connections are able to capture the initial elastic stiffness, decompression load, and residual stiffness under lateral loading. Then, analysis of a reduced-scale three-storey two-bay PT steel frame is carried out with sufficient accuracy by incorporating the proposed joint model into the frame analysis. The proposed method is then applied to assessing progressive collapse of a one-storey two-bay PT frame under middle column removal scenario, and is verified against existing experimental and ANSYS finite element results. Three resistance mechanism for progressive collapse of the PT frame are evaluated, which consists of angle flexural mechanism, beam compression arching action and strand tensile catenary action. Finally, parameter analyses of the PT frames are conducted to investigate the effects of the connection details on the behavior and resistance of progressive collapse. The proposed model can be used to predict the quasi-static behavior of PT frames under monotonic vertical loading conditions with satisfactory accuracy.

Original languageEnglish
Pages (from-to)481-493
Number of pages13
JournalSteel and Composite Structures
Volume40
Issue number4
DOIs
StatePublished - 25 Aug 2021

Keywords

  • Component-based method
  • Posttensioned connection
  • Progressive collapse
  • SAP2000
  • Steel frame

Fingerprint

Dive into the research topics of 'Component-based model for posttensioned steel connections against progressive collapse'. Together they form a unique fingerprint.

Cite this