Coupling physics in artificial neural network to predict the fatigue behavior of corroded steel wire
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OpenAlex topics: Mechanical stress and fatigue analysis · Infrastructure Maintenance and Monitoring · Structural Health Monitoring Techniques
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References (43)
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1. Structural health monitoring methods of cables in cable-stayed bridge: A review10.1016/j.measurement.2020.108343 ↗
2. Fatigue Resistance of Large-Diameter Cable for Cable-Stayed Bridges10.1061/(ASCE)0733-9445(1992)118:3(701) ↗
3. Environmental Factors Affecting Corrosion of Galvanized Steel Wires10.1061/(ASCE)0899-1561(2004)16:1(1) ↗
4. Time-Dependent Effect of Corrosion on the Mechanical Characteristics of Stay Cable10.1061/(ASCE)BE.1943-5592.0001229 ↗
5. Defect Characterization Method for Bridge Cables Based on Topology of Dynamical Reconstruction of Magnetostrictive Guided Wave Testing Signals10.1007/s10921-023-00940-2 ↗
6. Corrosion characterization of steel wires based on persistent homology theory for magnetostrictive guided wave testing signal. Structural Health Monitoring-an
7. Accelerated Corrosion and Embrittlement of High-Strength Bridge Wire10.1061/(ASCE)0899-1561(2000)12:1(33) ↗
8. Cracking and Fracture of Suspension Bridge Wire10.1061/(ASCE)1084-0702(2001)6:6(645) ↗
9. The fracture mechanisms of main cable wires from the forth road suspension10.1016/j.engfailanal.2013.01.025 ↗
10. Influence of corrosion rate on tensile strength of pcstrands
11. Experimental Study on Fatigue Strength of Corroded Bridge Wires10.1061/(ASCE)BE.1943-5592.0000366 ↗
12. Experimental and analytical study on fatigue strength and stress concentration of corroded bridge wires10.3233/BRS-160101 ↗
13. Experimental study on the fatigue behavior of corroded steel wire10.1016/j.jcsr.2020.106375 ↗
14. Experiments on factors affecting fatigue performance of bridge cable steel wires
15. Experimental study on fatigue performance of corroded high-strength steel wires used in bridges10.1016/j.conbuildmat.2018.07.249 ↗
16. Fatigue life prediction for parallel-wire stay cables considering corrosion effects10.1016/j.ijfatigue.2018.05.020 ↗
17. Predictive model for fatigue life in parallel-wire stay cables considering corrosion variability10.1080/15732479.2021.1994616 ↗
18. Probabilistic fatigue life prediction of corroded PC beams considering spatial variability of pitting corrosion10.1016/j.engstruct.2024.117721 ↗
19. Application of Machine Learning in Steel Materials: A Survey
20. Machine-learning-based investigation into the effect of defect/inclusion on fatigue behavior in steels10.1016/j.ijfatigue.2021.106597 ↗
21. Defect criticality analysis on fatigue life of L-PBF 17–4 PH stainless steel via machine learning10.1016/j.ijfatigue.2022.107018 ↗
22. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures10.1016/j.engfracmech.2021.108130 ↗
23. Fatigue life evaluation model for various austenitic stainless steels at elevated temperatures via alloy features-based machine learning approach10.1111/ffe.13895 ↗
24. Research on the Diffusion Model of Cable Corrosion Factors Based on Optimized BP Neural Network Algorithm
25. Data-driven prediction of the fatigue performance of corroded high-strength steel wires10.1016/j.engfailanal.2023.107108 ↗
26. Physics-Informed Neural Networks for Missing Physics Estimation in Cumulative Damage Models: A Case Study in Corrosion Fatigue10.1115/1.4047173 ↗
27. Data-driven approach to very high cycle fatigue life prediction10.1016/j.engfracmech.2023.109630 ↗
28. Physics-informed Neural Networks: Recent Advances and Prospects
29. Wang · 2023 · 284
30. Learning internal representations by error propagation. Parallel Distribted Porcessing Explorations in the Microstructures of
31. FEED-FORWARD NEURAL NETWORKS TRAINING: A COMPARISON BETWEEN GENETIC ALGORITHM AND BACK-PROPAGATION LEARNING ALGORITHM
32. Degradation of Mechanical Behavior of Corroded Prestressing Wires Subjected to High-Cycle Fatigue Loading10.1061/(ASCE)BE.1943-5592.0001030 ↗
33. Corrosion-Fatigue Analysis of High-Strength Steel Wire by Experiment and the Numerical Simulation10.3390/met10060734 ↗
34. A survey of outlier detection methodologies10.1023/B:AIRE.0000045502.10941.a9 ↗
35. Zhang · 2017
36. An Investigation on Disparity Responds of Machine Learning Algorithms to Data Normalization Method10.14500/aro.10970 ↗
37. A state-of-the-art review on fatigue life prediction methods for metal structures10.1007/s007730200012 ↗
38. ANALYSIS OF HIDDEN NODES FOR MULTILAYER PERCEPTRON NEURAL NETWORKS10.1016/0031-3203(94)90170-8 ↗
39. Prediction of Damage Factor in end Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network
40. Fatigue life evaluation model for high-strength steel wire considering different levels of corrosion10.1080/15732479.2021.1951773 ↗
41. Probabilistic physics-guided machine learning for fatigue data analysis10.1016/j.eswa.2020.114316 ↗
42. Statistical distribution patterns in mechanical and fatigue properties of metallic materials
43. Corrosion fatigue mechanisms and evaluation methods of high-strength steel wires: A state-of-the-art review10.1111/ffe.14311 ↗