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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:

Direct Shear Strength Prediction for Precast Concrete Joints Using the Machine Learning Method
Tongxu Liu, Zhen Wang, Zilin Long, et al.
Journal of Bridge Engineering (2022) Vol. 27, Iss. 5
Closed Access | Times Cited: 18

Showing 18 citing articles:

Prediction of shear strength in UHPC beams using machine learning-based models and SHAP interpretation
Meng Ye, Lifeng Li, Doo‐Yeol Yoo, et al.
Construction and Building Materials (2023) Vol. 408, pp. 133752-133752
Closed Access | Times Cited: 50
Explainable machine learning model for predicting punching shear strength of FRC flat slabs
Tongxu Liu, Celal Çakıroğlu, Kamrul Islam, et al.
Engineering Structures (2023) Vol. 301, pp. 117276-117276
Closed Access | Times Cited: 22
Explainable machine learning models for punching shear capacity of FRP bar reinforced concrete flat slab without shear reinforcement
Jia Yan, Jie Su, Jinjun Xu, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03162-e03162
Open Access | Times Cited: 10
Exploring explicit formula for shear transfer strength of concrete joints using dictionary learning
Tongxu Liu, Cheng Zhao, Zhen Wang
Construction and Building Materials (2025) Vol. 462, pp. 140000-140000
Closed Access
A Review of Punching Shear Strength in FRP-Reinforced Concrete Slab-Column Connections
Ragheb Salim
Turkish Journal of Civil Engineering (2025) Vol. 36, Iss. 3
Closed Access
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
Predicting the drift capacity of precast concrete columns using explainable machine learning approach
Zhen Wang, Tongxu Liu, Zilin Long, et al.
Engineering Structures (2023) Vol. 282, pp. 115771-115771
Closed Access | Times Cited: 11
Data-driven model to predict the residual drift of precast concrete columns
Zhen Wang, Tongxu Liu, Zilin Long, et al.
Journal of Building Engineering (2024) Vol. 85, pp. 108650-108650
Closed Access | Times Cited: 3
Concrete Spalling Identification and Fire Resistance Prediction for Fired RC Columns Using Machine Learning-Based Approaches
Thuan N.-T. Ho, Phuoc Trong Nguyen, Gia Toai Truong
Fire Technology (2024) Vol. 60, Iss. 3, pp. 1823-1866
Closed Access | Times Cited: 3
Interpretable ensemble machine learning models for predicting the shear capacity of UHPC joints
Meng Ye, Lifeng Li, Weimeng Jin, et al.
Engineering Structures (2024) Vol. 315, pp. 118443-118443
Closed Access | Times Cited: 3
Machine learning-based corrosion rate prediction of steel embedded in soil
Dong Zheng, Ling Ding, Meng Zhou, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Cyclic shear behavior and BoBiLSTM-based model for soil-rock mixture-concrete interfaces
Feiyu Liu, Chenbo Gao, Jinming Xu, et al.
Construction and Building Materials (2024) Vol. 426, pp. 136031-136031
Closed Access | Times Cited: 2
A robust approach to shear strength prediction of reinforced concrete deep beams using ensemble learning with SHAP interpretability
Achyut Tiwari, Ashok Kumar Gupta, Tanmay Gupta
Soft Computing (2023) Vol. 28, Iss. 7-8, pp. 6343-6365
Closed Access | Times Cited: 6
Predicting compressive strength of green concrete using hybrid artificial neural network with genetic algorithm
Lei Pan, Yuanfeng Wang, Kai Li, et al.
Structural Concrete (2022) Vol. 24, Iss. 2, pp. 1980-1996
Closed Access | Times Cited: 10
Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques
Congzhen Xiao, Baojuan Qiao, Jianhui Li, et al.
Advances in Civil Engineering (2022) Vol. 2022, Iss. 1
Open Access | Times Cited: 5
Determination of Reduction Factor of Shear Key Configurations for Calculating Direct Shear Strength of Precast Concrete Dry Joints Using Parametric Finite-Element Simulations
Tongxu Liu, Zhen Wang, Jingquan Wang, et al.
Journal of Bridge Engineering (2024) Vol. 29, Iss. 7
Closed Access
Research on wireless monitoring system and algorithm for preload force utilizing machine learning and electromechanical impedance
Zhiqiang Dong, Luhao Xia, Jinpeng Feng, et al.
Smart Materials and Structures (2024) Vol. 33, Iss. 9, pp. 095006-095006
Closed Access
Shallow Learning vs. Deep Learning in Engineering Applications
Fereshteh Jafari, Kamran Moradi, Qobad Shafiee
(2024), pp. 29-76
Closed Access
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