A modified physics-informed neural network to fatigue life prediction of deck-rib double-side welded joints
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OpenAlex topics: Fatigue and fracture mechanics · Welding Techniques and Residual Stresses · Non-Destructive Testing Techniques
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References (50)
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1. Evaluation on root-deck fatigue of orthotropic steel bridge deck[J]10.1016/j.jcsr.2013.07.036 ↗
2. Study on fatigue performance of steel bridge welded joints considering initial defects[J]10.1016/j.jcsr.2023.108309 ↗
3. A new orthotropic steel deck system incorporating two novel structural details[J]10.1016/j.jcsr.2022.107633 ↗
4. Some considerations in the statistical determination of the shape of SN curves[M]//Statistical analysis of fatigue data. ASTM
5. Accuracy of determined S-N curve for constructional steel by selected models[J]10.1111/ffe.13139 ↗
6. Experimental study on the determination of welding residual stress in rib-deck weld by sharp indentation testing[J]10.1016/j.tws.2021.107516 ↗
7. Fatigue life evaluation for deck-rib welding details of orthotropic steel deck integrating mean stress effects[J]10.1061/(ASCE)BE.1943-5592.0001344 ↗
8. An exponential stress function for predicting fatigue strength and life due to mean stresses[J]10.1016/S0142-1123(01)00044-5 ↗
9. Evaluation of an energy-based fatigue approach considering mean stress effects[J]10.1007/s12206-013-1155-y ↗
10. Fatigue evaluation of rib-to-deck welded joints of orthotropic steel bridge deck[J]10.1061/(ASCE)BE.1943-5592.0000181 ↗
11. High cycle fatigue SN curve prediction of steels based on transfer learning guided long short term memory network[J]10.1016/j.ijfatigue.2022.107050 ↗
12. A machine-learning fatigue life prediction approach of additively manufactured metals[J]10.1016/j.engfracmech.2020.107508 ↗
13. A neural network approach to fatigue life prediction[J]10.1016/j.ijfatigue.2010.09.003 ↗
14. Physics-informed neural networks for missing physics estimation in cumulative damage models: a case study in corrosion fatigue[J]10.1115/1.4047173 ↗
15. Probabilistic physics-guided machine learning for fatigue data analysis[J]10.1016/j.eswa.2020.114316 ↗
16. A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries[J]10.1063/5.0033376 ↗
17. Understanding deep convolutional networks[J]10.1098/rsta.2015.0203 ↗
18. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations[J]10.1126/science.aaw4741 ↗
19. Experimental investigation for structural parameter effects on fatigue behavior of rib-to-deck welded joints in orthotropic steel decks[J]10.1016/j.engfailanal.2017.04.028 ↗
20. Fatigue modeling using neural networks: A comprehensive review[J]10.1111/ffe.13640 ↗
21. Data-driven algorithm for real-time fatigue life prediction of structures with stochastic parameters[J]10.1016/j.cma.2020.113373 ↗
22. A physics-informed neural network approach to fatigue life prediction using small quantity of samples[J]10.1016/j.ijfatigue.2022.107270 ↗
23. Experiment on fatigue behavior of rib-to-deck weld root in orthotropic steel decks[J]10.1016/j.jcsr.2015.11.014 ↗
24. Fatigue strength and crack growth of double-side welded rib-to-deck joint in orthotropic steel decks[J]10.1016/j.jcsr.2022.107444 ↗
25. Fatigue assessment of U-rib full penetration welded joints based on local methods[J]10.1016/j.jcsr.2022.107684 ↗
26. An experimental investigation into fatigue behaviors of single-and double-sided U rib welds in orthotropic bridge decks[J]10.1016/j.ijfatigue.2022.106827 ↗
27. Fatigue resistance of orthotropic steel deck system with double-side welded rib-to-deck joint[J]10.1177/13694332221146858 ↗
28. The exponential law of endurance tests[C]//Proc Am Soc Test
29. Effective-notch-stress-based fatigue evaluation of rib-deck welds integrating the full-range S—N curve concept[J]10.1016/j.jcsr.2021.106541 ↗
30. Mean stress effects in strain–life fatigue[J]10.1111/j.1460-2695.2009.01404.x ↗
31. Fatigue durability evaluation of trough to deck plate welded joint of orthotropic steel deck[J]10.2208/jsceja.64.603 ↗
32. Influence of structural parameters on the stress of deck-U rib joint weld[J]
33. Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions[J]10.1016/j.renene.2020.06.043 ↗
34. Digital twin-driven intelligent assessment of gear surface degradation10.1016/j.ymssp.2022.109896 ↗
35. Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics[J]10.1016/j.ymssp.2023.110544 ↗
36. Data-driven prognostic scheme for bearings based on a novel health indicator and gated recurrent unit network[J]10.1109/TII.2022.3169465 ↗
37. Fatigue performance of rib-to-deck double-side welded joints in orthotropic steel decks[J]10.1016/j.engfailanal.2019.07.015 ↗
38. Fatigue failure and optimization of double-sided weld in orthotropic steel bridge decks[J]10.1016/j.engfailanal.2020.104750 ↗
39. Time-variant fatigue reliability assessment of rib-to-deck welded joints using ANN-based methods[C]//Structures
40. Machine Learning-Assisted probabilistic fatigue evaluation of Rib-to-Deck joints in orthotropic steel decks[J]10.1016/j.engstruct.2022.114496 ↗
41. Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods[J]
42. Physics-informed machine learning for low-cycle fatigue life prediction of 316 stainless steels[J]10.1016/j.ijfatigue.2024.108187 ↗
43. Fatigue damage assessment of orthotropic steel deck using dynamic Bayesian networks[J]10.1016/j.ijfatigue.2018.08.037 ↗
44. Low-cycle fatigue parameters and fatigue life estimation of high-strength steels with artificial neural networks[J]10.1111/ffe.13847 ↗
45. An investigation of artificial neural network structure and its effects on the estimation of the low-cycle fatigue parameters of various steels[J]10.1111/ffe.14054 ↗
46. Study on fatigue life of high-strength steel rebars joined by flash butt welding based on experimental and machine learning approaches[J]10.1016/j.engfailanal.2023.107812 ↗
47. Generative adversarial nets[J]
48. Kingma D P, Welling M. Auto-encoding variational bayes[J]. arxiv preprint arxiv:1312.6114, 2013. Doi: 10.48550/arXiv.1312.6114.
49. 3D reconstruction of digital cores based on a model using generative adversarial networks and variational auto-encoders[J]10.1016/j.petrol.2021.109151 ↗
50. Using variational auto encoding in credit card fraud detection[J]10.1109/ACCESS.2020.3015600 ↗