Skip to main navigation Skip to search Skip to main content

Compressive behavior of coal gangue concrete-filled steel tube in cold region towards to sustainability design

  • Tong Zhang
  • , Ruixue Zhu
  • , Shan Gao*
  • *Corresponding author for this work
  • Liaoning Technical University
  • China University of Mining and Technology
  • Xijing University

Research output: Contribution to journalArticlepeer-review

Abstract

To investigate the axial compression mechanical performance degradation mechanism of the coal gangue concrete-filled steel tubes (CGCFST) in cold region, the variation laws of concrete strength, coal gangue replacement rate, and the number of freeze-thaw cycle on its axial compressive mechanical properties were revealed through experiments. The results show that freeze-thaw cycles did not change the failure mode of CGCFST, with the increase in the number of freeze-thaw cycles, the angle between the shear-slip line and the horizontal line decreased. Freeze-thaw cycles led to a significant reduction in the ultimate load-bearing capacity, ductility, and initial stiffness of the specimens. The frost heave damage of the high-strength concrete specimens is more pronounced. For C50 specimens, the maximum reductions in ultimate load-bearing capacity, ductility and initial stiffness are 21.4 %, 69.6 % and 33.6 %, respectively. A constrained constitutive model of the coal gangue concrete with freeze-thaw damage was proposed through debonding analysis, and a axial compression finite element model was established for CGCFST in cold region. Finite element analysis indicates that increasing the steel ratio and concrete strength can effectively reduce freeze-thaw damage. When the number of freeze-thaw cycles was 100, 200, 300, and 400, increasing the steel ratio from 1 % to 5 % resulted in a maximum increase of 82.3 % in ultimate load-bearing capacity. Based on the testing and finite element data, the ultimate load-bearing capacity and the stiffness design method for CGCFST in cold region was proposed, with errors within 10 %. The training results of the constructed AGDO-SVM machine learning prediction model show that the model can more accurately predict the mechanical properties of CGCFST after freeze-thaw cycles, with determination coefficients greater than 0.99 for both the training and testing sets.

Original languageEnglish
Article number145002
JournalConstruction and Building Materials
Volume506
DOIs
StatePublished - 13 Jan 2026

Keywords

  • AGDO-SVM
  • Axial compressive properties
  • Coal gangue aggregate
  • Concrete-filled steel tube
  • Freeze-thaw cycle
  • Machine Learning

Fingerprint

Dive into the research topics of 'Compressive behavior of coal gangue concrete-filled steel tube in cold region towards to sustainability design'. Together they form a unique fingerprint.

Cite this