Predicting local buckling in steel members under cyclic loads: a strain-based approach using the CSM
Author(s)
Chen, Yu
Málaga-Chuquitaype, Christian
Zhou, Feng
Gardner, Leroy
Type
Journal Article
Abstract
A new approach, based on the Continuous Strength Method (CSM), is presented for the prediction of cross-section failure through inelastic local buckling in steel members under cyclic loading. Cross-section local buckling failure is predicted to occur when a prescribed strain limit is reached; the effect of loading history is accounted for by considering the cumulative strain from each loading cycle. The strain limit is related to the cross-section slenderness, which is based on the local buckling behaviour of the full cross-section, rather than the individual plate width-to-thickness ratios, making it suitable for different section profiles (e.g., hollow sections and I-sections). Finite element models are established, validated and used to conduct parametric studies, covering a series of steel grades, a wide range of cross-section slendernesses and various cyclic axial loading protocols. The resulting numerical data sets are employed to develop a means of rationally considering the contribution of both the compressive and tensile strain ranges in each loading cycle to cross-section failure. The suitability of the newly proposed method is verified against experimental results on beam-columns with hollow sections and I-sections under cyclic loading, as well as numerical results. The findings indicate that the cumulative deformation capacity and local buckling failure of steel cross-sections can be accurately predicted using the developed strain-based design method. The method can be applied in the inelastic analysis of structures under seismic loading and in the seismic design of steel cross-sections.
Date Issued
2025-12-15
Date Acceptance
2025-09-26
Citation
Engineering Structures, 2025, 345 (Part A)
ISSN
0141-0296
Publisher
Elsevier BV
Journal / Book Title
Engineering Structures
Volume
345
Issue
Part A
Copyright Statement
Copyright © 2025 Elsevier Ltd. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Accepted
Article Number
121473
Date Publish Online
2025-10-02