Digital‐Image‐Correlation for Simulating Cyclic Local Buckling in Steel Beams

Abstract: Nonlinear continuum finite element (CFE) analyses rely on accurate multiaxial constitutive law formulations along with reliable imperfection patterns for simulating nonlinear geometric instabilities in steel members under mechanical loading. Validations of these models usually rely on conventional measurements of macroscopic parameters (e.g., displacement and strain field). It is common to use global deformation characteristics (e.g., deduced moment versus chord rotation) for model validation purposes of steel members exhibiting inelastic cyclic local buckling. The reason is that measurements from conventional strain gauge readings are not deemed to be reliable well before the onset of local buckling. This is potentially problematic when the accurate prediction of local strain demands is essential. This paper discusses the experimental results from a steel beam instrumented with a digital image correlation (DIC) system and how optical measurements acquired from DIC can be used to benchmark a CFE model representation of this beam. It is shown that while different CFE model types simulate accurately the moment rotation relationship of the steel beam, local strain demand predictions could vary considerably between model types. The results suggest that DIC measurements can inform the selection of proper imperfection patterns to be used in CFE models for the reliable estimation of inelastic strain demands.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Digital‐Image‐Correlation for Simulating Cyclic Local Buckling in Steel Beams ; volume:6 ; number:3-4 ; year:2023 ; pages:1717-1722 ; extent:6 ; ce/papers ; 6, Heft 3-4 (2023), 1717-1722 (gesamt 6)

Contributors, Places and Time

• Creator: Ozden, Selimcan ; de Castro e Sousa, Albano ; Lignos, Dimitrios

Further information

• DOI: 10.1002/cepa.2671
• URN: urn:nbn:de:101:1-2023091215314515666432
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2024, 1:57 AM CEST