Improvement of Cyclic Void Growth Model for Ultra-Low Cycle Fatigue Prediction of Steel Bridge Piers

Abstract

The cyclic void growth model (CVGM) is a micro-mechanical fracture model that has been used to assess ultra-low cycle fatigue (ULCF) of steel structures in recent years. However, owing to the stress triaxiality range and contingency of experimental results, low goodness of fit is sometimes obtained when calibrating the model damage degradation parameter, resulting in poor prediction. In order to improve the prediction accuracy of the CVGM model, a model parameter calibration method is proposed. In the research presented in this paper, tests were conducted on circular notched specimens that provided different magnitudes of stress triaxiality. The comparative analysis was carried out between experimental results and predicted results. The results indicate that the number of cycles and the equivalent plastic strain to ULCF fracture initiation by the CVGM model calibrated by the proposed method agree well with the experimental results. The proposed parameter calibration method greatly improves prediction accuracy compared to the previous method.