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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:

Estimating dynamic compressive strength of rock subjected to freeze-thaw weathering by data-driven models and non-destructive rock properties
Shengtao Zhou, Yu Lei, Zong‐Xian Zhang, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-24
Closed Access | Times Cited: 12

Showing 12 citing articles:

Bayesian optimization-enhanced ensemble learning for the uniaxial compressive strength prediction of natural rock and its application
Chukwuemeka Daniel, Xin Yin, Xing Huang, et al.
Geohazard Mechanics (2024) Vol. 2, Iss. 3, pp. 197-215
Open Access | Times Cited: 7
Optimization of an Artificial Neural Network Using Four Novel Metaheuristic Algorithms for the Prediction of Rock Fragmentation in Mine Blasting
Ahsan Rabbani, Divesh Ranjan Kumar, Yewuhalashet Fissha, et al.
Journal of The Institution of Engineers (India) Series D (2024)
Closed Access | Times Cited: 5
Predicting corrosion behaviour of steel reinforcement in eco-friendly coral aggregate concrete based on hybrid machine learning methods
Zhen Sun, Yalin Li, Li Su, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-21
Closed Access | Times Cited: 4
Prediction of Shear Strength in Anisotropic Structural Planes Considering Size Effects
Wei Ma, Wenhao Zou, Jinlong Zhang, et al.
Designs (2025) Vol. 9, Iss. 1, pp. 17-17
Open Access
Shear strength parameters prediction of rock materials using hybrid machine learning model
Yanhui Cheng, Dongliang He, Hongwei Liu, et al.
Nondestructive Testing And Evaluation (2025), pp. 1-24
Closed Access
A machine learning framework for predicting shear strength properties of rock materials
Daxing Lei, Yaoping Zhang, Zhigang Lu, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
A novel identification technology and real-time classification forecasting model based on hybrid machine learning methods in mixed weathered mudstone-sand-pebble formation
Yong Zeng, Yingjie Wei, Yuyou Yang
Tunnelling and Underground Space Technology (2024) Vol. 153, pp. 106045-106045
Closed Access | Times Cited: 2
Microseismic Data-Driven Short-Term Rockburst Evaluation in Underground Engineering with Strategic Data Augmentation and Extremely Randomized Forest
Shouye Cheng, Xin Yin, Feng Gao, et al.
Mathematics (2024) Vol. 12, Iss. 22, pp. 3502-3502
Open Access | Times Cited: 2
Surrounding Rock Squeezing Classification in Underground Engineering Using a Hybrid Paradigm of Generative Artificial Intelligence and Deep Ensemble Learning
Shouye Cheng, Xin Yin, Feng Gao, et al.
Mathematics (2024) Vol. 12, Iss. 23, pp. 3832-3832
Open Access | Times Cited: 1
Failure mechanisms of coarse-grained sandstone under pure mode I/II loading: insights from energy evolution and acoustic emission
Zelin Liu, Chunde Ma, Xin’ao Wei, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-21
Closed Access
Empirical relationship of characteristic impedance with rock drillability and cuttability
Zong‐Xian Zhang, Shengtao Zhou, Zhao Wang, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-25
Closed Access
Development of the Split-Hopkinson Pressure Bar and Its Application in Testing the Dynamic Mechanical Properties of Quasi-Brittle Materials: A Review
Shizhuo Zou, Yongtao Gao, Zehua Yang, et al.
Journal of Materials Research and Technology (2024)
Open Access
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