journal article·2025-08

Fatigue life prediction method based on polynomial chaotic expansion and Weibull distribution

GaoFei Ji, LingHui Hu

International Journal of Fracture· Volume 249· Issue 3
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: Fatigue and fracture mechanics · Structural Health Monitoring Techniques · Mechanical Failure Analysis and Simulation

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

References (48)

Show source-provided references
1. Cao ZC, Zhang WZ (2023) Low cycle fatigue and fatigue-creep interaction effects on softening behavior and life prediction of cast aluminum alloy at elevated temperature. Mater Today Commun 36:106796. https://doi.org/10.1016/j.mtcomm.2023.10679610.1016/j.mtcomm.2023.106796 ↗
2. Cao SJ, Zhang HZ, Hu JX et al (2024) Fatigue life prediction model for shot-peened laser powder bed fused 304L steel considering residual stress relaxation and defect distribution. Eng Fail Anal 162:108423. https://doi.org/10.1016/j.engfailanal.2024.10842310.1016/j.engfailanal.2024.108423 ↗
3. Chen D, Li YZ, Liu K et al (2023) A physics-informed neural network approach to fatigue life prediction using small quantity of samples. Int J Fatigue 166:107270. https://doi.org/10.1016/j.ijfatigue.2022.10727010.1016/j.ijfatigue.2022.107270 ↗
4. Chen YF, Wang XW, Zhang Z et al (2024) An approach to estimate the low cycle fatigue probabilistic curves of PBF-LB/M 316L steel from small size datasets using the remora optimization algorithm. Int J Fatigue 185:108375. https://doi.org/10.1016/j.ijfatigue.2024.10837510.1016/j.ijfatigue.2024.108375 ↗
5. Ding TT, Liu SS, Wang ZJ et al (2024) A novel mixture sampling strategy combining latin hypercube sampling with optimized one factor at a time method: a case study on mixtures of antibiotics and pesticides. J Hazard Mater 461:132568. https://doi.org/10.1016/j.jhazmat.2023.13256810.1016/j.jhazmat.2023.132568 ↗
6. Dong XL, Wang DL, Lu JY et al (2024) A wind power forecasting model based on polynomial chaotic expansion and numerical weather prediction. Electric Power Syst Res 227:109983. https://doi.org/10.1016/j.epsr.2023.10998310.1016/j.epsr.2023.109983 ↗
7. Fang J, Li KY, Liu CX et al (2024) A novel two-scale nonlinear damage accumulation model for vibration fatigue life prediction. Int J Non-Linear Mech 166:104796. https://doi.org/10.1016/j.ijnonlinmec.2024.10476910.1016/j.ijnonlinmec.2024.104769 ↗
8. Gan L, Wu H, Zhong Z et al (2023) On the integration of domain knowledge and branching neural network for fatigue life prediction with small samples. Int J Fatigue 172:107648. https://doi.org/10.1016/j.ijfatigue.2023.10764810.1016/j.ijfatigue.2023.107648 ↗
9. Gomes RC, Araújo JA, Cardoso RA et al (2023) Fatigue life prediction using critical distance on aluminum alloy wire containing indentation produced marks. Theoret Appl Fract Mech 128:104135. https://doi.org/10.1016/j.tafmec.2023.10413510.1016/j.tafmec.2023.104135 ↗
10. Guo YC, Pan HM, Shen AQ et al (2023) Fracture properties of basalt-fiber-reinforced bridge concrete under dynamic fatigue loading. Structures 56:10501810.1016/j.istruc.2023.105018 ↗
11. Hai LT, Ban HY, Yang XF et al (2023) Low-cycle fatigue behavior of hot-rolled titanium-clad bimetallic steel. Int J Mech Sci 254:108443. https://doi.org/10.1016/j.ijmecsci.2023.10844310.1016/j.ijmecsci.2023.108443 ↗
12. Hu ZY, Hua L, Mei ZY et al (2024) Corrosion fatigue life prediction model for 10CrNiCu shipbuilding steel under authentic marine environments. Case Stud Construct Mater 21:e03787. https://doi.org/10.1016/j.cscm.2024.e0378710.1016/j.cscm.2024.e03787 ↗
13. Hua FL, Liu JH, Pan XM et al (2023) Research on multiaxial fatigue life of notched specimens based on Weibull distribution and Bayes estimation. Int J Fatigue 166:107271. https://doi.org/10.1016/j.ijfatigue.2022.10727110.1016/j.ijfatigue.2022.107271 ↗
14. Hung V, Ramona T, Thanh B et al (2021) Probabilistic method for time-varying reliability analysis of structure via variational bayesian neural network. Structures 34:3703–371510.1016/j.istruc.2021.09.069 ↗
15. Jan H, Jiří B, Petr H et al (2023) Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach. Int J Fatigue 169:1074–1083
16. Li W, Cheng LY, Zeng F et al (2023a) High-temperature fatigue life improvement of small-deep holes by using a novel cold expansion process in a nickel-based superalloy. J Manuf Process 106:393–40610.1016/j.jmapro.2023.09.067 ↗
17. Li H, Luo X, Zhang YQ et al (2023b) Viscoelastic fracture mechanics-based fatigue life model in asphalt-filler composite system. Eng Fract Mech 292:10958910.1016/j.engfracmech.2023.109589 ↗
18. Li RH, Li C, Li HN et al (2023c) Improved estimation on seismic behavior of RC column members: a probabilistic method considering dynamic effect and structural parameter uncertainties. Struct Saf 101:1023–102810.1016/j.strusafe.2022.102308 ↗
19. Lian ZH, Li MJ, Lu WC (2022) Fatigue life prediction of aluminum alloy via knowledge-based machine learning. Int J Fatigue 157:106716. https://doi.org/10.1016/j.ijfatigue.2021.10671610.1016/j.ijfatigue.2021.106716 ↗
20. Lindström SB, Moverare J, Xu JH et al (2023) Service-life assessment of aircraft integral structures based on incremental fatigue damage modeling. Int J Fatigue 172:107600. https://doi.org/10.1016/j.ijfatigue.2023.10760010.1016/j.ijfatigue.2023.107600 ↗
21. Liu M, Chen XZ, Yang QS (2016) Characteristics of dynamic pressures on a saddle type roof in various boundary layer flows. J Wind Eng Ind Aerodyn 150:1–14. https://doi.org/10.1016/j.jweia.2015.11.01210.1016/j.jweia.2015.11.012 ↗
22. Liu JH, Hua FL, Zhao H et al (2022) Research on fatigue life evaluation method of shafts based on small sample P-S-N. Int J Press Vessels Pip 199:104773. https://doi.org/10.1016/j.ijpvp.2022.10477310.1016/j.ijpvp.2022.104773 ↗
23. Liu YK, Fan JL, Zhu G et al (2023) Data-driven approach to very high cycle fatigue life prediction. Eng Fract Mech 292:109630. https://doi.org/10.1016/j.engfracmech.2023.10963010.1016/j.engfracmech.2023.109630 ↗
24. Liu JH, Zhang ZY, Zhu LX et al (2023) Research on fatigue life evaluation method of shaft parts based on small sample. Int J Struct Integr 15:709–732. https://doi.org/10.1108/IJSI-06-2023-005010.1108/IJSI-06-2023-0050 ↗
25. Mirzaei AH, Haghi P, Shokrieh MM (2024) Prediction of fatigue life of laminated composites by integrating artificial neural network model and non-dominated sorting genetic algorithm. Int J Fatigue 188:108528. https://doi.org/10.1016/j.ijfatigue.2024.10852810.1016/j.ijfatigue.2024.108528 ↗
26. Mohammad A, Khonsari M (2023) On the effect of environmental temperature on fracture fatigue entropy. Int J Fatigue 168:10741110.1016/j.ijfatigue.2022.107411 ↗
27. Osterman G, Lesch S, Bradford SA (2024) A conditioned Latin hypercube sampling design methodology for ground-truthing transient EM resistivity models. Comput Geosci 187:105582. https://doi.org/10.1016/j.cageo.2024.10558210.1016/j.cageo.2024.105582 ↗
28. Park SJ (2023) Application of ductile fracture model for the prediction of low cycle fatigue in structural steel. Eng Fract Mech 289:10946910.1016/j.engfracmech.2023.109469 ↗
29. Park JP, Ham J, Mohanty S et al (2023) Statistical analysis of SN type environmental fatigue data of Ni-base alloy welds using weibull distribution. Nucl Eng Technol 55(5):1924–1934. https://doi.org/10.1016/j.net.2022.08.00510.1016/j.net.2022.08.005 ↗
30. Pei HQ, Shi LJ, Ma MQ et al (2024) Fatigue fracture mechanism and life assessment for irregular film cooling hole structures in Ni-based single crystal turbine blades. Eng Fract Mech 310:110506. https://doi.org/10.1016/j.engfracmech.2024.11050610.1016/j.engfracmech.2024.110506 ↗
31. Rosanne JT, Alexander L, Peter DG (2024) Generic E-variables for exact sequential k-sample tests that allow for optional stopping. J Stat Plan Inference 230:106116. https://doi.org/10.1016/j.jspi.2023.10611610.1016/j.jspi.2023.106116 ↗
32. Safar A, Jalali S, Silva L et al (2023) Effects of low cycle impact fatigue on the residual mode II fracture energy of adhesively bonded joints. Int J Adhes Adhes 126:10345510.1016/j.ijadhadh.2023.103455 ↗
33. Shen ZJ, Su YC, Deng JC et al (2024) Investigation of high-cycle fatigue property and fatigue crack propagation behavior of a die-forged 2014 aluminum alloy aircraft wheel. Int J Fatigue 184:108309. https://doi.org/10.1016/j.ijfatigue.2024.10830910.1016/j.ijfatigue.2024.108309 ↗
34. Song HP, Liu J, Zhang H et al (2023) Multi-source data driven fatigue failure analysis and life prediction of pre-corroded aluminum–lithium alloy 2050–T8. Eng Fract Mech 292:109626. https://doi.org/10.1016/j.engfracmech.2023.10962610.1016/j.engfracmech.2023.109626 ↗
35. Usabiaga H, Calvente MM, Ramalle M et al (2020) Improving with probabilistic and scale features the Basquin linear and bi-linear fatigue models. Eng Fail Anal 116:104728. https://doi.org/10.1016/j.engfailanal.2020.10472810.1016/j.engfailanal.2020.104728 ↗
36. Vasu A, Mei J, Chung J et al (2023) Using Weibull distribution function to determine the design bounds for carburized 4320 steel shafts subjected to bending dominated fatigue loading. Int J Fatigue 168:107447. https://doi.org/10.1016/j.ijfatigue.2022.10744710.1016/j.ijfatigue.2022.107447 ↗
37. Wang JL, Peng WJ, Liu TL et al (2024a) The fatigue behavior of TC17 titanium alloy under different test environments: The effect of saltwater on fatigue failure characteristics and life estimation. Int J Fatigue 187:108461. https://doi.org/10.1016/j.ijfatigue.2024.10846110.1016/j.ijfatigue.2024.108461 ↗
38. Wang JX, Jin CT, Gao S (2024b) Anti-collapse on the fragility and sensitivity analysis of composite frame with CFST columns based on the improved Latin hypercube sampling. Structures 63:106402. https://doi.org/10.1016/j.istruc.2024.10640210.1016/j.istruc.2024.106402 ↗
39. Wu SL, Liu JH, Wang YZ et al (2024) Multiaxial low-cycle fatigue life model for notched specimens considering small sample characteristics. Int J Struct Integr 15(5):948–97310.1108/IJSI-05-2024-0071 ↗
40. Xiao HJ, Song T, Jia BH et al (2022) Uncertainty analysis of MSD crack propagation based on polynomial chaos expansion. Theoret Appl Fract Mech 120:103390. https://doi.org/10.1016/j.tafmec.2022.10339010.1016/j.tafmec.2022.103390 ↗
41. Xiao X, Cheng DH, Li HT et al (2024) Microstructure analysis of 7050 aluminum alloy joint fabricated by linear friction weld. Int J Lightweight Mater Manuf 7(4):520–530. https://doi.org/10.1016/j.ijlmm.2024.03.00810.1016/j.ijlmm.2024.03.008 ↗
42. Xie LC, Ren SY, Yin F et al (2023) Effects of three stress levels on the fatigue properties and fracture mechanisms of an in-situ synthesized TiB/Ti-6Al-4V titanium matrix composite. Mater Charact 195:11251110.1016/j.matchar.2022.112511 ↗
43. Zhang YJ, Su R, Ma HK et al (2024) Investigation on fatigue performance and residual stress release mechanism of split sleeve cold expansion holes of heterogeneous 7050–T7451 aluminum alloy. J Alloy Compd 1007:176446. https://doi.org/10.1016/j.jallcom.2024.17644610.1016/j.jallcom.2024.176446 ↗
44. Zhang ZY, Liu JH, Wu SC et al (2025) Probabilistic fatigue life prediction of small sample properties notched specimens under multiaxial loading. Theoret Appl Fract Mech 136:104836. https://doi.org/10.1016/j.tafmec.2024.10483610.1016/j.tafmec.2024.104836 ↗
45. Zhao CY, Wang JJ, He FX et al (2025) A fatigue life prediction method based on multi-signal fusion deep attention residual convolutional neural network. Appl Acoust 235:110646. https://doi.org/10.1016/j.apacoust.2025.11064610.1016/j.apacoust.2025.110646 ↗
46. Zheng SK, Shen J, Wang W et al (2023) Multi-twinned deformation and fracture characteristics of directional solidified Ti-45.5Al–5Nb-0.5Ta alloys during high-temperature rotary-bending fatigue process. Mater Sci Eng: A 876:14515710.1016/j.msea.2023.145157 ↗
47. Zhu SP, Liu Q, Peng WW et al (2018) Computational-experimental approaches for fatigue reliability assessment of turbine bladed disks. Int J Mech Sci 142–143:502–51710.1016/j.ijmecsci.2018.04.050 ↗
48. Zhu SP, Wu YL, Yi XJ et al (2022) Probabilistic fatigue assessment of notched components under size effect using g eneralized weakest-link model. Int J Fatigue 162:10700510.1016/j.ijfatigue.2022.107005 ↗