Enhanced Voids Growth Model for Ductile Fracture Prediction of High-Strength Steel Q690D under Monotonic Tension: Experiments and Numerical Simulation

AbstractAccurate prediction of progressive damage and failure of structural steels is a nontrivial issue in practical design and assessment of steel structural buildings. It appears far more important in the fracture-oriented scenarios under extreme environmental actions or accidental overloading co...

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Bibliographic Details
Published in:Journal of structural engineering (New York, N.Y.) Vol. 146; no. 6
Main Authors: Kong, De-Yang, Yang, Bo
Format: Journal Article
Language:English
Published: New York American Society of Civil Engineers 01-06-2020
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Summary:AbstractAccurate prediction of progressive damage and failure of structural steels is a nontrivial issue in practical design and assessment of steel structural buildings. It appears far more important in the fracture-oriented scenarios under extreme environmental actions or accidental overloading conditions because, in these cases, the ultimate strength of steel structures is directly governed by material failure. However, previous studies on ductile fracture of structural steels were relatively limited, especially for high-strength steels (HSSs) like Q690D steel. In this paper, an enhanced fracture model based on the voids growth model (VGM) is proposed. The normalized maximum shear stress is introduced to make it capable of capturing ductile fracture at low and moderate stress triaxialities. To calibrate and verify the newly proposed model, a series of tensile tests of smooth and notched round bar specimens and shear specimens with different shear angles were carried out by using Q690D steel. Bridgeman’s theory was applied to identify the postnecking constitutive relationship with the help of a three-dimensional digital image correlation (3D-DIC) system. The good agreements between numerical simulations and experimental results finally verified the applicability and reliability of the enhanced model in ductile fracture prediction.
ISSN:0733-9445
1943-541X
DOI:10.1061/(ASCE)ST.1943-541X.0002658