1. Fitting fatigue test data with a novel S-N curve using frequentist and Bayesian inference
- Author
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Johan Maljaars, H.H. Snijder, Davide Leonetti, Aluminium Structures, and Steel Structures
- Subjects
Percentile ,Physics::Medical Physics ,Bayesian inference ,02 engineering and technology ,Upper and lower bounds ,Constant amplitude loading ,Industrial and Manufacturing Engineering ,Computer Science::Robotics ,0203 mechanical engineering ,SR - Structural Reliability ,Frequentist inference ,Statistics ,Applied mathematics ,General Materials Science ,Mathematics ,TS - Technical Sciences ,Mechanical Engineering ,Buildings and Infrastructures ,021001 nanoscience & nanotechnology ,Fatigue limit ,Architecture and Building ,020303 mechanical engineering & transports ,S-N curves ,Mechanics of Materials ,2015 Urbanisation ,Modeling and Simulation ,Curve fitting ,2015 Fluid & Solid Mechanics ,0210 nano-technology ,Constant (mathematics) ,Test data ,Maximum likelihood - Abstract
In design against fatigue, a lower bound stress range vs. endurance curve (S-N curve) is employed to characterize fatigue resistance of plain material and structural details. With respect to the inherent variability of the fatigue life, the S-N curve is related to a certain probability of exceedance, a percentile of the fatigue life. This paper is concerned with modelling and estimating uncertainties in fatigue resistance of welded joints under constant amplitude loading. A new S-N curve format is proposed and fitted to fatigue test data by using the Maximum Likelihood Method. The results have been compared with the Random Fatigue Limit Model and the Bilinear Random Fatigue Limit Model. The proposed S-N curve appears to be more accurate in describing the S-N relation in high-cycle fatigue: it presents a smooth transition from finite to infinite-life regions and, differently from previous non-linear S-N relations with fatigue limit, this transition is controlled by an independent model parameter. Thereby it provides more flexibility for statistical fitting. In addition, a Bayesian framework is defined to fit the proposed relation including informative and non-informative prior distributions. © 2017 Elsevier Ltd
- Published
- 2017
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