Digital Twin-driven framework for fatigue life prediction of steel bridges using a probabilistic multiscale model: Application to segmental orthotropic steel deck specimen

Abstract

Accurate fatigue life prediction facilitates the fatigue maintenance of steel bridges. Since Digital Twin can simulate the lifecycle for physical objects at various scales, this study aims to provide a Digital Twin-driven framework for non-deterministic fatigue life prediction of steel bridges. A probabilistic multiscale model was developed to depict the fatigue evolution throughout the bridge lifecycle. The small crack initiation period was well described by the modified Fine and Bhat model considering microstructure uncertainties. After obtaining the critical model parameter via crystal plastic finite element simulation, the modified model was further calibrated using the assumed historical fatigue data in Digital Twin database. Based on the initiated half-penny-shaped small crack, the small crack initiation period was connected to the macrocrack extension period. Given the uncertainties of macrocrack propagation, the Paris’ law with random growth parameters was adopted. The Bayesian inference of the growth parameters realized the real-time calibration of the macrocrack growth model using Markov chain Monte Carlo simulation. The feasibility of the proposed framework was demonstrated through fatigue tests on a segmental steel deck specimen with mixed-mode deformed U-rib to diaphragm welded joints. The results show that the predicted fatigue initiation life and residual fatigue life are in good agreement with the experimentally observed life results. In summary, the proposed framework enhances our understanding of the fatigue evolution mechanism throughout the bridge lifecycle and provides an entirely new approach to accurately predict the fatigue life of steel bridges under various sources of uncertainties.