Surface Roughness Characterization for Stress Concentration Factor Predictions: A Bayesian Learning Approach

References (31)

1. 1 Bayesian Data Analysis [DOI]
2. 2 An Introduction to MCMC for Machine Learning [DOI]
3. 3 Theory of Stress Concentration for Shear-Strained Prismatical Bodies With Arbitrary Nonlinear Stress-Strain Law [DOI]
4. 4 Peterson's Stress Concentration Factors [DOI]
5. 5 Machine Learning: A Bayesian and Optimization Perspective
6. 6 Data Analysis: A Bayesian Tutorial [DOI]
7. 7 The effect of machined topography and integrity on fatigue life [DOI]
8. 8 Modelling the influence of machined surface roughness on the fatigue life of aluminium alloy [DOI]
9. 9 Recent developments of Bayesian model class selection and applications in civil engineering [DOI]
10. 10 Bayesian system identification based on hierarchical sparse Bayesian learning and Gibbs sampling with application to structural damage assessment [DOI]
11. 11 An Examination of the Effects from Surface Texture on the Strength of Fiber Reinforced Plastics [DOI]
12. 12 Surface roughness characterization for fatigue life predictions using finite element analysis [DOI]
13. 13 Value of information from vibration-based structural health monitoring extracted via Bayesian model updating [DOI]
14. 14 An introduction to Monte Carlo methods [DOI]
15. 15 Transfer-learning guided Bayesian model updating for damage identification considering modeling uncertainty [DOI]
16. 16 Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler [DOI]
17. 17 Effect of surface finish on fatigue strength [DOI]
18. 18 Fatigue life prediction of machined components using finite element analysis of surface topography [DOI]
19. 19 A computer model for fatigue crack growth from rough surfaces [DOI]
20. 20 Quantifying Registration Uncertainty With Sparse Bayesian Modelling [DOI]
21. 21 Sparse Bayesian learning with model reduction for probabilistic structural damage detection with limited measurements [DOI]
22. 22 Efficient Bayesian updating with two-step adaptive Kriging [DOI]
23. 23 Geometrical characterization of surface roughness and its application to fatigue crack initiation [DOI]
24. 24 Gibbs posterior inference on multivariate quantiles [DOI]
25. 25 Effects of surface roughness and strain range on the low-cycle fatigue behavior of type 304 stainless steel [DOI]
26. 26 Characterization of the topography of turned surfaces and its influence on the fatigue life of Al-7075 [DOI]
27. 27 Hierarchical Bayesian model for the transfer of knowledge on spatial concepts based on multimodal information [DOI]
28. 28 Measurement of echo reduction for passive-material samples using sparse Bayesian learning and least squares estimation [DOI]
29. 29 An efficient approximation-based robust design optimization framework for large-scale structural systems [DOI]
30. 30 Online Bayesian learning for mixtures of spatial spline regressions with mixed effects [DOI]
31. 31 Variational Relevance Vector Machines [DOI]

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