4.5 Article

A probabilistic fatigue approach for riveted joints using Monte Carlo simulation

期刊

JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH
卷 110, 期 -, 页码 149-162

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jcsr.2015.02.019

关键词

Riveted joints; Puddle iron; Fatigue crack initiation; Fatigue crack propagation; Probabilistic approach; Monte Carlo simulation

资金

  1. Portuguese Science Foundation (FCT) [SFRH/BD/66497/2009, SFRH/BD/72434/2010]
  2. Fundação para a Ciência e a Tecnologia [SFRH/BD/66497/2009, SFRH/BD/72434/2010] Funding Source: FCT

向作者/读者索取更多资源

The availability of probabilistic fatigue strength S-N data for riveted connections is essential to carry out reliability analysis of ancient riveted bridges. This paper proposes a procedure to derive probabilistic S-N fields for riveted connections, using material fatigue data and detailed finite element modeling of the joints. Strain-life fatigue data of the plain material as well as fatigue crack growth data are used to compute the total fatigue life of a riveted connection, integrating both the local and Fracture Mechanics approaches to fatigue. The basic fatigue data is inputted in the probabilistic form as well as some relevant parameters of the model which are subjected to higher uncertainty. Three dimensional finite element modeling of the riveted joint is carried out to assess the local stresses/strains at the critical location, as well as the stress intensity factor history, for an expected growing fatigue crack. The proposed approach allows the assessment of the effects of both clamping stresses on rivets and friction coefficient, on local stress/strain values and stress intensity factors. The clamping stresses on rivets and friction coefficient are assumed random variables. The inputs, in the probabilistic form, are accounted in the fatigue strength assessment procedure using the Monte Carlo sampling technique. Resulting p-S-N-f field computed for a simple riveted joint is compared with experimental data, illustrating a very satisfactory performance of the model. In addition, the sensibility of the p-S-N-f field to some input parameters is discussed. (C) 2015 Elsevier Ltd. All rights reserved.

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