Machine Learning-Assisted probabilistic fatigue evaluation of Rib-to-Deck joints in orthotropic steel decks

This study integrates the fatigue test and numerical prediction to derive a comprehensive probability-stress-life (P-S-N) curve for rib-to-deck (RD) welded joints in orthotropic steel decks. Fatigue tests of RD joints are con-ducted to measure fatigue strength and crack growth data. Based on the test, a probabilistic fatigue crack growth (PFCG) model is established to predict the distribution of fatigue life under various stress ranges. Two machine learning tools are adopted to assist the PFCG model-based prediction, i.e., the Gaussian process regression (GPR) and dynamic Bayesian network (DBN). The GPR is used to train a surrogate model solving stress intensity factors for the PFCG prediction, using 2,000 samples generated from finite element (FE) analyses. The trained model is then validated by a new dataset of 100 FE samples. An adapted DBN model is proposed to update the PFCG model with the fatigue crack growth data measured from ten specimens. According to the result, the application of GPR can reduce the solution cost of the PFCG prediction by approximately 1,875 times. Compared with the prior PFCG model, the updated posterior model shows an improved agreement with the test data, i.e., the maximum difference in fatigue strength between model prediction and test data decreases from 12% to 3%. Based on the posterior PFCG model, the P-S-N curve of RD joints is statistically derived using sufficient numerical samples.

Automated summary

This study integrates fatigue testing and numerical prediction to derive a comprehensive probability-stress-life (P-S-N) curve for rib-to-deck (RD) welded joints in orthotropic steel decks. The study establishes a probabilistic fatigue crack growth (PFCG) model and uses machine-learning tools to assist in the prediction. The results show improved agreement between the model prediction and test data, with a significant reduction in solution cost.