journal article·2023-07

Structural fatigue reliability analysis based on active learning Kriging model

Hua-Ming Qian, Jing Wei, Hong-Zhong Huang

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Probabilistic and Robust Engineering Design · Structural Health Monitoring Techniques · Advanced Multi-Objective Optimization Algorithms

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (38)

Show source-provided references
1. A novel classification method to random samples for efficient reliability sensitivity analysis10.1115/1.4054769 ↗
2. New efficient and robust method for structural reliability analysis and its application in reliability-based design optimization10.1016/j.cma.2020.113018 ↗
3. An efficient method for time-variant reliability including finite element analysis10.1016/j.ress.2021.107534 ↗
4. Time-variant reliability analysis for a complex system based on active-learning Kriging model10.1061/AJRUA6.RUENG-962 ↗
5. Rain-flow fatigue damage for transformed Gaussian loads10.1016/j.ijfatigue.2006.05.006 ↗
6. Palmgren-Miner's rule and fracture mechanics-based inspection planning10.1016/j.engfracmech.2011.08.002 ↗
7. Cumulative damage in fatigue10.1115/1.4009458 ↗
8. A stress-based model for fatigue life prediction of high density polyethylene under complicated loading conditions10.1016/j.ijfatigue.2018.10.007 ↗
9. Fatigue life calculation procedure for the rotor of an embedded magnet traction motor taking into account thermomechanical loads10.1016/j.ymssp.2018.03.055 ↗
10. Fatigue life prediction of composite tidal turbine blades10.1016/j.oceaneng.2022.111903 ↗
11. Effective stress and fatigue life prediction with mean stress correction models on a ferritic stainless steel sheet10.1016/j.ijfatigue.2021.106707 ↗
12. An efficient online time-temperature-dependent creep-fatigue rainflow counting algorithm10.1016/j.ijfatigue.2018.06.037 ↗
13. Numerical study of fatigue damage under random loading using Rainflow cycle counting10.1108/IJSI-04-2020-0036 ↗
14. Load spectrum generation of machining center based on rainflow counting method10.21595/jve.2017.18423 ↗
15. Probabilistic modeling of uncertainties in fatigue reliability analysis of turbine bladed disks10.1016/j.ijfatigue.2020.105912 ↗
16. A probabilistic fatigue life prediction for adhesively bonded joints via ANNs-based hybrid model10.1016/j.ijfatigue.2021.106352 ↗
17. Probabilistic fatigue model for design and life extension of mooring chains, including mean load and corrosion effects10.1016/j.oceaneng.2021.110396 ↗
18. Probabilistic fatigue life prediction of bearings via the generalized polynomial chaos expansion10.1007/s12206-022-0902-3 ↗
19. Probabilistic fatigue life prediction and reliability assessment of a high pressure turbine disc considering load variations10.1177/1056789517737132 ↗
20. Computational-experimental approaches for fatigue reliability assessment of turbine bladed disks10.1016/j.ijmecsci.2018.04.050 ↗
21. Fatigue reliability analysis of concrete structures based on physical synthesis method10.1016/j.probengmech.2019.02.003 ↗
22. Fatigue reliability analysis for structures with known loading trend10.1007/s00158-013-1044-0 ↗
23. Fatigue reliability assessment of welded steel bridge decks under stochastic truck loads via machine learning10.1061/(ASCE)BE.1943-5592.0000982 ↗
24. Probabilistic machine learning approach to bridge fatigue failure analysis due to vehicular overloading10.1016/j.engstruct.2019.05.028 ↗
25. Fatigue modeling using neural networks: a comprehensive review10.1111/ffe.13640 ↗
26. AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation10.1016/j.strusafe.2011.01.002 ↗
27. Time-variant system reliability analysis method for a small failure probability problem10.1016/j.ress.2020.107261 ↗
28. System reliability analysis based on dependent Kriging predictions and parallel learning strategy10.1016/j.ress.2021.108083 ↗
29. Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables10.1016/j.cma.2019.112649 ↗
30. Probabilistic life assessment on fatigue crack growth in mixed-mode by coupling of Kriging model and finite element analysis10.1016/j.engfracmech.2015.03.040 ↗
31. An application of active learning Kriging for the failure probability and sensitivity functions of turbine disk with imprecise probability distributions10.1007/s00366-021-01366-y ↗
32. Reliability-based combined high and low cycle fatigue analysis of turbine blade using adaptive least squares support vector machines
33. Life reliability assessment of twin-web disk using the active learning Kriging model10.1007/s00158-019-02395-4 ↗
34. Determination of fragments of multiaxial service loading strongly influencing the fatigue of machine components10.1016/j.ymssp.2009.05.010 ↗
35. Nielsen · 2002
36. Reliability analysis with Monte Carlo simulation and dependent Kriging predictions10.1115/1.4034219 ↗
37. Active learning method combining Kriging model and multimodal-optimization-based importance sampling for the estimation of small failure probability10.1002/nme.6495 ↗
38. A novel active learning method for profust reliability analysis based on the Kriging model10.1007/s00366-021-01447-y ↗