Digital twin Bayesian entropy framework for corrosion fatigue life prediction and calibration of bridge suspender

Abstract

This paper proposes an intelligent digital twin framework for corrosion fatigue life prediction and calibration of suspender wires integrated with mechanism-driven, sensor-driven, and information fusion. A general probabilistic information fusion strategy is constructed to handle entropy-based external constraints and classical Bayesian updating. Statistical moment, range bound, and point data are considered to investigate the effect of various types and sequences of information. A small-time domain fatigue crack growth model is proposed to overcome the limitations of traditional cycle-based methods, which can capture the large and small cycles of random fatigue stress. The virtual sensor-based stress time-history response is obtained under different traffic flow densities through digital twin finite element model of a suspension bridge. The results show that with and without considering interval bound leads to different fatigue life prediction results, especially for statistical moment data fusion, and the maximum difference is approximately 54%. The average prediction life of suspender wires is gradually close to the actual service life as crack observations increase. The standard deviations of the corrosion fatigue life decrease by 88%, when simultaneously integrating moment, interval, and point data.