Iraqi Journal of Civil Engineering
Login
Iraqi Journal of Civil Engineering
  • Home
  • Articles & Issues
    • Latest Issue
    • All Issues
  • Authors
    • Submit Manuscript
    • Guide for Authors
    • Authorship
    • Article Processing Charges (APC)
  • Reviewers
    • Guide for Reviewers
    • Become a Reviewer
    • Reviewers of IJCE
  • About
    • About Journal
    • Aims and Scope
    • Editorial Team
    • Journal Insights
    • Peer Review Process
    • Publication Ethics
    • Plagiarism
    • Allegations of Misconduct
    • Appeals and Complaints
    • Corrections and Withdrawals
    • Open Access
    • Archiving Policy
    • Journal Funding Sources
    • AI Usage and Disclosure Policy
    • Announcements
    • Contact

Search Results for composite-column

Article
Nonlinear 3D Finite Element Model for Square Composite Columns Under Various Parameters

DARA MAHMOOD, Serwan Rafiq, Muhammed Adbullah

Pages: 19-28

PDF Full Text
Abstract

Composite columns are frequently used in constructing high-rise structures because they can minimize the size of the building's columns while increasing the floor plan's usable space. This study aims to create a nonlinear 3D finite element model for square composite columns designed for solid and hollow columns with various multi-skin tubes subjected to loads at eccentricities of (30 and 60) mm, compressive strength, and mesh size using the ABAQUS software. The comparison was based on the experimental data of six references of composite columns. While the compressive strength of concrete increases, the stiffness of the composite column rise. The ratio of concrete compressive strength values for composite column increased by (0, 12.3, 17.8, and 26.7 percent) for (fc'=25, 31.96, 35, and 40) MPa, respectively. The results of the different mesh sizes (20, 40, and 60) mm are showing; The experimental results and the finite element solution developed using the (20 X20) mm element correspond well. The nonlinear finite element analysis method was used, and the finite element outputs results were confirmed to be in favorable agreement with the experimental data

Article
Experimental Behavior of High Strength Concrete Filled Double Skin Steel Tubular Columns

Samoel Mahdi Saleh, Fareed Hameed Majeed

Pages: 75-85

PDF Full Text
Abstract

A series of experimental tests were carried out to investigate the behavior of high strength concrete filled double skin steel tubular (HSCFDST) columns. Fourteen column specimens were tested in the present study, taking into account the effects of the shape of column cross section (circular or square), the hollowness ratio, and the slenderness ratio. For comparison, two of the tested specimens were filled with normal strength concrete. It was seen that the ultimate axial strength of the square HSCFDST columns is greater than that for circular ones, in spite of that the sectional properties were approximately equal. Also, it was found that for both circular and square column specimens, the ultimate axial strength of HSCFDST columns was inversely proportional to their hollowness and slenderness ratios. CFDST column specimens filled with high strength concrete compared with those filled with normal strength concrete increased stiffness and ultimate axial strength, but give unexpected results for the ultimate axial strength, therefore the suitable choice for the section properties of the inner steel tube is required. The experimental results and analytical approach that developed by other researchers shown good agreement.

1 - 2 of 2 items

Search Parameters

Journal Logo
Iraqi Journal of Civil Engineering

University of Anbar

  • Copyright Policy
  • Terms & Conditions
  • Privacy Policy
  • Accessibility
  • Cookie Settings
Licensing & Open Access

CC BY 4.0 Logo Licensed under CC-BY-4.0

This journal provides immediate open access to its content.

Editorial Manager Logo Elsevier Logo

Peer-review powered by Elsevier’s Editorial Manager®

       
Copyright © 2025 College of Engineering, University of Anbar. All rights reserved, including those for text and data mining, AI training, and similar technologies.