Material characterization using finite element deletion strategies for collapse modeling of steel structures

The prediction of collapse of structures has gained growing attention recently, as it is important to be able to predict and model structural collapse due to extreme loads. A lack of accurate, pragmatic, and validated structural collapse models currently limits the capabilities for predicting collapse due to possible extreme loads. This research compares three finite element deletion strategies that account implicitly for fracture under monotonic loading to be used as predictive tools for collapse modeling of steel structures. The first strategy employs a Void Growth Model (VGM) to simulate the initiation of softening and the Hillerborg model for modeling of material softening, followed by an element deletion strategy that is developed in this framework. The second strategy adds a Bao-Wierzbicki model to the VGM strategy (VGM-BW) in order to account more directly for fracture initiation in lower and negative triaxiality regions. The third strategy is a constant critical strain (CS) approach that does not include softening but instead deletes an element when it achieves a peak equivalent plastic strain. The parameters of the VGM strategy were calibrated to a comprehensive set of experimental test results of circumferentially notched tensile (CNT) coupon specimens, the Bao-Wierzbicki parameters in VGM-BW strategy were determined analytically through tensile coupon (TC) specimens, and the CS approach used a constant value for equivalent plastic strain at softening initiation. These strategies were then validated through comparison with experimental test results of specimens commonly used for material characterization of steel. The results establish the accuracy and effectiveness of the VGM strategy for high-fidelity parametric simulation capabilities for collapse of steel structures and provide recommendations for where additional experimental research is needed to validate regions of low and negative triaxiality. (C) 2017 Elsevier Ltd. All rights reserved.