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author:

Chen, Weihong (Chen, Weihong.) [1] (Scholars:陈伟宏) | Xie, Yujun (Xie, Yujun.) [2] | Guo, Xiaohui (Guo, Xiaohui.) [3] | Li, Dong (Li, Dong.) [4] (Scholars:李栋)

Indexed by:

Scopus SCIE

Abstract:

Prefabricated beam-column connections are the most vulnerable components of prefabricated buildings during earthquake events. The seismic performance of the beam-column connection is functional as the critical component plays a key role in structural safety. This study aimed to develop a novel hybrid prefabricated concrete (HPC) connection, combining with wet and dry connection techniques, to enhance the seismic performance of prefabricated concrete frames. A quasi-static experimental investigation was carried out to examine the seismic performance of the proposed connection. Two full-scale prefabricated connection specimens utilizing the proposed HPC connection and another code-defined monolithic prefabricated concrete (PC) connection were tested under cyclic loading, keeping the axial load on the column constant. The ductility, stiffness degradation, energy dissipation capacity, post-tensioned force, and residual displacement were obtained based on the experimental output. The results indicated that the HPC connection developed had high construction efficiency and better seismic performance than the conventional PC connection. The strength and energy dissipation capacity were significantly improved by up to 52% and 10%, respectively. The cracking and stiffness degradation were well-controlled.

Keyword:

beam-column connection energy dissipation capacity hybrid connection post-tensioned seismic performance

Community:

  • [ 1 ] [Chen, Weihong]Fuzhou Univ, Coll Civil Engn, Fuzhou 350116, Peoples R China
  • [ 2 ] [Xie, Yujun]Fuzhou Univ, Coll Civil Engn, Fuzhou 350116, Peoples R China
  • [ 3 ] [Li, Dong]Fuzhou Univ, Coll Civil Engn, Fuzhou 350116, Peoples R China
  • [ 4 ] [Chen, Weihong]China Earthquake Adm, Key Lab Bldg Collapse Mech & Disaster Prevent, Sanhe 065201, Peoples R China
  • [ 5 ] [Guo, Xiaohui]Yango Univ, Coll Artificial Intelligence, Fuzhou 350015, Peoples R China

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Source :

BUILDINGS

ISSN: 2075-5309

Year: 2022

Issue: 6

Volume: 12

3 . 8

JCR@2022

3 . 1 0 0

JCR@2023

ESI Discipline: ENGINEERING;

ESI HC Threshold:66

JCR Journal Grade:2

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count: 6

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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