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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods
Haijie Wang, Bo Li, Jian‐Guo Gong, et al.
Engineering Fracture Mechanics (2023) Vol. 284, pp. 109242-109242
Closed Access | Times Cited: 104

Showing 26-50 of 104 citing articles:

Advancing Fatigue Life Prediction with Machine Learning: A review
Atef Hamada, Shaimaa Elyamny, Walaa Abd‐Elaziem, et al.
Materials Today Communications (2025), pp. 111525-111525
Closed Access
Fatigue Life Prediction of 2024-T3 Clad Al Alloy Based on an Improved SWT Equation and Machine Learning
Zhaoji Li, Weibing Dai, Haitao Yue, et al.
Materials (2025) Vol. 18, Iss. 2, pp. 332-332
Open Access
A data-informed knowledge discovery framework to predict fatigue properties of additively manufactured Ti–6Al–4V, IN718 and AlSi10Mg alloys using fatigue databases
Samuel Onimpa Alfred, Mehdi Amiri
Progress in Additive Manufacturing (2025)
Open Access
Synergizing machine learning and multiscale shakedown method for shakedown loading capacity evaluation of parameterized lattice structures
Lizhe Wang, Fuyuan Liu, Min Chen, et al.
Extreme Mechanics Letters (2025) Vol. 75, pp. 102297-102297
Closed Access
Data-driven nonlocal damage mechanics and fracture of shells
Daoping Liu, Xuejiao Shao, Xiaolong Fu, et al.
Engineering Fracture Mechanics (2025), pp. 110864-110864
Closed Access
Mechanism- and data-driven based dynamic hybrid modeling for multi-condition processes
Yanan Zhang, Gaowei Yan, Shuyi Xiao, et al.
Chemometrics and Intelligent Laboratory Systems (2025), pp. 105353-105353
Closed Access
Microstructural feature-based physics-informed neural network for creep residual life prediction of P91 steel
Zhi Liu, Zhou Zheng, Peng Zhao, et al.
Engineering Fracture Mechanics (2025), pp. 110989-110989
Closed Access
A Hybrid Physics-Informed and Data-Driven Approach for Predicting the Fatigue Life of Concrete Using an Energy-Based Fatigue Model and Machine Learning
Himanshu Rana, Adnan Ibrahimbegović
Computation (2025) Vol. 13, Iss. 3, pp. 61-61
Open Access
A TCN-based feature fusion framework for multiaxial fatigue life prediction: Bridging loading dynamics and material characteristics
Peng Zhang, Keke Tang
International Journal of Fatigue (2025), pp. 108915-108915
Closed Access
Effect of thickness on fatigue crack initiation of nickel-based single crystal superalloy micro notched specimens at 850 °C
Jundong Wang, Wenqing Wu, Xiangqian Xu, et al.
Engineering Fracture Mechanics (2025), pp. 111054-111054
Closed Access
Enhancing energy materials with data-driven methods: A roadmap to long-term hydrogen energy sustainability using machine learning
Cheng Li, Jianjun Ma, D. R. Gibson, et al.
International Journal of Hydrogen Energy (2025) Vol. 119, pp. 108-125
Closed Access
Research on the mechanical behavior of high-speed railway gear steel 20CrNi2Mo: Experimental and theoretical investigation
Lixing Ma, Bidan Huang, Fenglin Guo, et al.
Engineering Failure Analysis (2025), pp. 109533-109533
Closed Access
Machine learning-based prediction of hydrogen-assisted fatigue crack growth rate in Cr–Mo steel
Jiangchuan Hu, Kai Ma, Zhenquan Zhang, et al.
International Journal of Hydrogen Energy (2025) Vol. 122, pp. 1-11
Closed Access
Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network
Haijie Wang, Bo Li, Liming Lei, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109852-109852
Closed Access | Times Cited: 12
A deep learning-based approach for crack damage detection using strain field
Zekai Huang, Dongdong Chang, Xiaofa Yang, et al.
Engineering Fracture Mechanics (2023) Vol. 293, pp. 109703-109703
Closed Access | Times Cited: 11
Study on high cycle fatigue behaviours and modelling of cast aluminium alloy at elevated temperatures
Yuan‐Chang Liang, Zhengxing Zuo, Jundiao Wang, et al.
Engineering Failure Analysis (2024), pp. 109031-109031
Closed Access | Times Cited: 3
Microstructural feature-driven machine learning for predicting mechanical tensile strength of laser powder bed fusion (L-PBF) additively manufactured Ti6Al4V alloy
Haijie Wang, Bo Li, Wei Zhang, et al.
Engineering Fracture Mechanics (2023) Vol. 295, pp. 109788-109788
Closed Access | Times Cited: 10
Ballast Settlement Accumulation in Zones with Unsupported Sleepers
Olga Nabochenko, Mykola Sysyn, Szabolcs Fischer
Transportation Infrastructure Geotechnology (2024) Vol. 11, Iss. 4, pp. 2598-2637
Open Access | Times Cited: 3
A real-time solution method for three-dimensional steady temperature field of transformer windings based on mechanism-embedded cascade network
Yunpeng Liu, Qingxian Zhao, Gang Liu, et al.
Case Studies in Thermal Engineering (2024) Vol. 58, pp. 104444-104444
Open Access | Times Cited: 3
Prediction of multiaxial fatigue life with a data-driven knowledge transfer model
Lei Gan, Zhi‐Ming Fan, Hao Wu, et al.
International Journal of Fatigue (2024), pp. 108636-108636
Closed Access | Times Cited: 3
Strategic optimization of engine performance and emissions with bio-hydrogenated diesel and biodiesel: A RVEA-GRNNs framework
Niti Klinkaew, Attasit Wiangkham, Atthaphon Ariyarit, et al.
Results in Engineering (2024) Vol. 24, pp. 103072-103072
Open Access | Times Cited: 3
Machine-Learning Synergy in High-Entropy Alloys: A Review
Sally Elkatatny, Walaa Abd‐Elaziem, Tamer A. Sebaey, et al.
Journal of Materials Research and Technology (2024) Vol. 33, pp. 3976-3997
Closed Access | Times Cited: 3
Deep Learning-Based Fatigue Strength Prediction for Ferrous Alloy
Zhikun Huang, Jingchao Yan, Jianlong Zhang, et al.
Processes (2024) Vol. 12, Iss. 10, pp. 2214-2214
Open Access | Times Cited: 3
A generalization ability-enhanced image recognition based multiaxial fatigue life prediction method for complex loading conditions
Tianguo Zhou, Xingyue Sun, Zhen Yu, et al.
Engineering Fracture Mechanics (2023) Vol. 295, pp. 109802-109802
Closed Access | Times Cited: 7
Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
Sadiq Gbagba, Lorenzo Maccioni, Franco Concli
Applied Sciences (2023) Vol. 14, Iss. 1, pp. 398-398
Open Access | Times Cited: 7

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