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Fatigue damage evaluation using S-N curves obtained by different data fitting methods

 Fatigue damage evaluation using S-N curves obtained by different data fitting methods
Auteur(s): , , ,
Présenté pendant IABSE Symposium: Engineering the Future, Vancouver, Canada, 21-23 September 2017, publié dans , pp. 2551-2558
DOI: 10.2749/vancouver.2017.2551
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This paper investigates the differences in the predicted fatigue life of transverse butt weld joints and cover-plated beams considering statistically based fitting methods on fatigue test data and ...
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Détails bibliographiques

Auteur(s): (Eindhoven University of Technology, Eindhoven, NB, The Netherlands)
(Eindhoven University of Technology, Eindhoven, NB, The Netherlands)
(TNO, Delft, ZH, The Netherlands)
(TNO, Delft, ZH, The Netherlands; Eindhoven University of Technology, Eindhoven, NB, The Netherlands)
Médium: papier de conférence
Langue(s): anglais
Conférence: IABSE Symposium: Engineering the Future, Vancouver, Canada, 21-23 September 2017
Publié dans:
Page(s): 2551-2558 Nombre total de pages (du PDF): 8
Page(s): 2551-2558
Nombre total de pages (du PDF): 8
Année: 2017
DOI: 10.2749/vancouver.2017.2551
Abstrait:

This paper investigates the differences in the predicted fatigue life of transverse butt weld joints and cover-plated beams considering statistically based fitting methods on fatigue test data and comparing the results with the S-N relation proposed in international standards. The test data fitting are performed by employing the Least Square Method (LSM) and the Maximum Likelihood Method (MLM) taking into account the contribution of the runouts. The Mean curve, the characteristic curve and the design curve have been derived. In order to consider the effect of Variable Amplitude loading, the S-N curves have been extended and the fatigue damage according to the Palmgren- Miner cumulative damage rule is evaluated for a load spectrum that follows a Rayleigh distribution. The results have been compared and discussed together with a Linear Elastic Fracture Mechanics (LEFM) based fatigue prediction.

Mots-clé:
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