Probabilistic modelling of residual stresses in cold-formed rectangular hollow sections

Research output: Contribution to journalArticleScientificpeer-review

1 Downloads (Pure)

Abstract

This study revisits residual stress models of cold-formed rectangular hollow sections (CFRHS). Residual stresses of CFRHS have a complex distribution that varies along the cross-sectional perimeter and through the material thickness. The distribution depends on the manufacturing methods and steel grades, which constantly evolve. Existing residual stress models are based on old measurements and for normal strength steel specimens (nominal yield strength fy,nom ≤ 460 MPa). This study evaluates the suitability of these models for modern CFRHS made of normal- and high-strength (fy,nom > 460 MPa) steels. Evaluation is carried out as an extensive analysis for a data set, which is collected from the literature, and supplemented with new measurements made for grade S700 specimens. As a result of the evaluation, an updated residual stress model is proposed, which combines the best suitable features of the existing models with slight modifications. The proposed model is valid for CFRHS made of steel grades up to S960. The model can be used in the advanced analyses of CFRHS structures. Additionally, statistical information is provided for the residual stress components such that the model can be used in probabilistic modelling and reliability studies.
Original languageEnglish
Article number107108
Number of pages18
JournalJournal of Constructional Steel Research
Volume189
Early online date29 Dec 2021
DOIs
Publication statusPublished - 2022
Publication typeA1 Journal article-refereed

Keywords

  • residual stress
  • cold-formed
  • Hollow section
  • high-strength steel
  • experimental investigation
  • reliability study

Publication forum classification

  • Publication forum level 2

Fingerprint

Dive into the research topics of 'Probabilistic modelling of residual stresses in cold-formed rectangular hollow sections'. Together they form a unique fingerprint.

Cite this