Print Email Facebook Twitter Machine Learning-Assisted probabilistic fatigue evaluation of Rib-to-Deck joints in orthotropic steel decks Title Machine Learning-Assisted probabilistic fatigue evaluation of Rib-to-Deck joints in orthotropic steel decks Author Heng, J. (TU Delft Steel & Composite Structures; Shenzhen University) Zheng, Kaifeng (Southwest Jiaotong University) Feng, Xiaoyang (Southwest Jiaotong University) Veljkovic, M. (TU Delft Steel & Composite Structures) Zhou, Zhixiang (Shenzhen University) Date 2022 Abstract 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 conducted 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. Subject dynamic Bayesian networkGaussian process regressionOrthotropic steel decksProbabilistic fatigue assessmentRib-to-deck joints To reference this document use: http://resolver.tudelft.nl/uuid:f0090251-0935-4a8c-bec8-5cacb424aec6 DOI https://doi.org/10.1016/j.engstruct.2022.114496 Embargo date 2022-12-09 ISSN 0141-0296 Source Engineering Structures, 265 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2022 J. Heng, Kaifeng Zheng, Xiaoyang Feng, M. Veljkovic, Zhixiang Zhou Files PDF 1_s2.0_S0141029622006046_main.pdf 6.35 MB Close viewer /islandora/object/uuid:f0090251-0935-4a8c-bec8-5cacb424aec6/datastream/OBJ/view