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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:

Fatigue life analysis of high-strength bolts based on machine learning method and SHapley Additive exPlanations (SHAP) approach
Shujia Zhang, Honggang Lei, Zichun Zhou, et al.
Structures (2023) Vol. 51, pp. 275-287
Closed Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Spatial and temporal variation of ecological quality in northeastern China and analysis of influencing factors
Xiaoyong Zhang, Weiwei Jia, Jinyou He
Journal of Cleaner Production (2023) Vol. 423, pp. 138650-138650
Closed Access | Times Cited: 32
Prediction of non-uniform shrinkage of steel-concrete composite slabs based on explainable ensemble machine learning model
Shiqi Wang, Jinlong Liu, Qinghe Wang, et al.
Journal of Building Engineering (2024) Vol. 88, pp. 109002-109002
Closed Access | Times Cited: 13
In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods
Shang Lou, Zhuohang Yu, Zejun Huang, et al.
Chemical Research in Toxicology (2024) Vol. 37, Iss. 3, pp. 513-524
Closed Access | Times Cited: 11
Neural network integrated with symbolic regression for multiaxial fatigue life prediction
Peng Zhang, Keke Tang, Anbin Wang, et al.
International Journal of Fatigue (2024) Vol. 188, pp. 108535-108535
Closed Access | Times Cited: 11
Optimizing fatigue life prediction of high strength bolts in bolted spherical joints
Bin Qiu, Tong Lan, Xuanzhe Ji, et al.
Journal of Constructional Steel Research (2024) Vol. 217, pp. 108635-108635
Closed Access | Times Cited: 8
AI-Driven Decision-Making Applications in Pharmaceutical Sciences
Bancha Yingngam, Abhiruj Navabhatra, Polpan Sillapapibool
Advances in media, entertainment and the arts (AMEA) book series (2024), pp. 1-63
Closed Access | Times Cited: 5
Development of a Machine Learning (ML)-Based Computational Model to Estimate the Engineering Properties of Portland Cement Concrete (PCC)
Rodrigo Polo-Mendoza, Gilberto Martínez-Arguelles, Rita Peñabaena‐Niebles, et al.
Arabian Journal for Science and Engineering (2024) Vol. 49, Iss. 10, pp. 14351-14365
Open Access | Times Cited: 5
Very high cycle fatigue life prediction of Ti60 alloy based on machine learning with data enhancement
Hongjiang Qian, Zhiyong Huang, Yeting Xu, et al.
Engineering Fracture Mechanics (2023) Vol. 289, pp. 109431-109431
Closed Access | Times Cited: 11
Exploring elastic properties of fly ash recycled aggregate concrete: Insights from multiscale modeling and machine learning
Maedeh Hosseinzadeh, Mehdi Dehestani, Alireza Hosseinzadeh
Structures (2023) Vol. 59, pp. 105720-105720
Closed Access | Times Cited: 11
Mechanical performance analysis of bolt connections for wind turbine towers after corrosion
Tingyun Wang, Gang Liang, Yunhe Liu
Structures (2025) Vol. 71, pp. 108202-108202
Closed Access
Common causes of failures in the industrial bolt and nut connections
Kazem Reza Kashyzadeh, Siamak Ghorbani, Al-Adarbi Marsel Kasimovich
Engineering Failure Analysis (2025), pp. 109431-109431
Closed Access
A data-driven approach to identify the optimal sub-laminates for homogeneity design under the concept of double-double composites
Cheng Qiu, Hongwei Song, Jinglei Yang
Composites Part A Applied Science and Manufacturing (2025), pp. 108897-108897
Closed Access
Predicting the efficiency of arsenic immobilization in soils by biochar using machine learning
Jin-Man Cao, Yu-Qian Liu, Yanqing Liu, et al.
Journal of Environmental Sciences (2023) Vol. 147, pp. 259-267
Closed Access | Times Cited: 10
Remaining fatigue life prediction of additively manufactured Inconel 718 alloy based on in-situ SEM and deep learning
Yixu Zhang, Ni Wang, Jianli Zhou, et al.
Engineering Failure Analysis (2024) Vol. 163, pp. 108440-108440
Closed Access | Times Cited: 3
A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints
Jian Wang, Qiuren Chen, Li Huang, et al.
Advances in Manufacturing (2024) Vol. 12, Iss. 3, pp. 538-555
Closed Access | Times Cited: 3
Bond strength and failure mode prediction model for recycled aggregate concrete based on intelligent algorithm optimized support vector machine
Congcong Fan, Youliang Ding, Yuanxun Zheng
Structures (2024) Vol. 71, pp. 107999-107999
Closed Access | Times Cited: 3
Failure mode-specific probabilistic bearing capacity of RC columns via interpretable Gaussian processes
Yu He, Kai Qian, Yafei Ma, et al.
Engineering Structures (2025) Vol. 329, pp. 119817-119817
Closed Access
Novel Machine Learning Modeling Approach for Fatigue Failure of Hydrogen-Transporting Pipelines
Nayem Ahmed, Ramadan Ahmed, Cătălin Teodoriu, et al.
SPE Journal (2025), pp. 1-20
Closed Access
Application of Artificial Intelligence to Support Design and Analysis of Steel Structures
Sina Sarfarazi, Ida Mascolo, Mariano Modano, et al.
Metals (2025) Vol. 15, Iss. 4, pp. 408-408
Open Access
Damage Classification of a Bolted Connection using Guided Waves and Explainable Artificial Intelligence
Muping Hu, Nan Yue, Roger M. Groves
Procedia Structural Integrity (2024) Vol. 52, pp. 224-233
Open Access | Times Cited: 3
Study on fatigue life of high-strength steel rebars joined by flash butt welding based on experimental and machine learning approaches
Xingwang Sheng, Chao Lin, Weiqi Zheng, et al.
Engineering Failure Analysis (2023) Vol. 156, pp. 107812-107812
Closed Access | Times Cited: 7
Machine Learning Accelerating the Condition Screening of Ceftriaxone Sodium Anaerobic Co-Metabolic Degradation
Hao Chen, Xinyuan Cao, Jinlong Wang, et al.
ACS ES&T Engineering (2024) Vol. 4, Iss. 4, pp. 947-955
Closed Access | Times Cited: 2
Creep–fatigue life prediction of a titanium alloy deep-sea submersible using a continuum damage mechanics-informed BP neural network model
Yuhao Guo, Shichao Wang, Gang Liu
Ocean Engineering (2024) Vol. 311, pp. 118826-118826
Closed Access | Times Cited: 2
Stress, Strain, or Energy? which one is superior predictor of fatigue life in notched Components? a novel Machine Learning-Based framework
A.M. Mirzaei
Engineering Fracture Mechanics (2024) Vol. 309, pp. 110401-110401
Open Access | Times Cited: 2
Machine learning approach for predicting and understanding fatigue crack growth rate of austenitic stainless steels in high-temperature water environments
Dayu Fajrul Falaakh, Jongweon Cho, Chi Bum Bahn
Theoretical and Applied Fracture Mechanics (2024) Vol. 133, pp. 104499-104499
Closed Access | Times Cited: 1
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