GPR-assisted evaluation of probabilistic fatigue crack growth in rib-to-deck joints in orthotropic steel decks considering mixed failure models. (1st February 2022)

• Record Type: Journal Article
• Authors: Heng, Junlin; Zhou, Zhixiang; Zou, Yang; Kaewunruen, Sakdirat
• Is Part Of: Engineering structures. Volume 252(2022)
• Publication Date: 2022-02-01
• Subjects: Orthotropic steel deck -- Rib-to-deck joint -- Mixed failure models -- Probabilistic fatigue crack growth -- Gaussian process regression; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2021.113688
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Probabilistic crack growth model is established for RD joints with mixed failure modes. Machine learning tool, Gaussian process regression, is used to boost the simulation. Effect of mixed failure models is revealed on the fatigue behaviour of RD joints. Uncertainty is quantified in failure model, fatigue life and crack growth of RD joints. Abstract: Rib-to-deck (RD) welded joints in orthotropic steel decks (OSDs) of bridges demonstrates two major fatigue failure models, including the toe-to-deck (TTD) cracking and root-to-deck (RTD) cracking. Generally, the sole failure model is employed in the fatigue assessment of RD joints, causing a hot dispute on the dominant failure model. In this paper, the fatigue crack growth (FCG) in RD joints has been evaluated considering uncertainties and mixed failure models. A probabilistic fatigue crack growth (PFCG) model is at first established for the RD joint, in which two crack-like initial flaws are assumed at the weld toe and root of the RD joint. After that, the gaussian process regression is used to assist and boost the PFCG simulation. Then, the PFCG model is implemented on a typical OSD with the random traffic model. Finally, the result of the PFCG model is discussed in detail, including the failure model, fatigue reliability and life prediction, and crack size evolution. It is revealed that both the TTD and RTD cracking models have a notable contribution to fatigue failure and could not be ignored. More crucial, a remarkable reduction can be observed in the fatigue reliability of RD joints when considering mixed failure models. This study not only highlights the influence of mixed failure models on the fatigue performance of welded joints, but also provide an insight into the application of novel machine learning tools in solving the traditional structural issue.