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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 Techniques to Predict Rock Strength Parameters
Arsalan Mahmoodzadeh, Mokhtar Mohammadi, Sirwan Ghafoor Salim, et al.
Rock Mechanics and Rock Engineering (2022) Vol. 55, Iss. 3, pp. 1721-1741
Closed Access | Times Cited: 66

Showing 1-25 of 66 citing articles:

Fusion of finite element and machine learning methods to predict rock shear strength parameters
Defu Zhu, Biaobiao Yu, Deyu Wang, et al.
Journal of Geophysics and Engineering (2024) Vol. 21, Iss. 4, pp. 1183-1193
Open Access | Times Cited: 86
Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models
Athanasia D. Skentou, Abidhan Bardhan, Anna Mamou, et al.
Rock Mechanics and Rock Engineering (2022) Vol. 56, Iss. 1, pp. 487-514
Open Access | Times Cited: 76
Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data
Panagiotis G. Asteris, Μαρία Καρόγλου, Athanasia D. Skentou, et al.
Ultrasonics (2024) Vol. 141, pp. 107347-107347
Closed Access | Times Cited: 48
Envisioning Human–Machine Relationship Towards Mining of the Future: An Overview
Peter Kolapo, Nafiu Olanrewaju Ogunsola, Kayode Komolafe, et al.
Mining (2025) Vol. 5, Iss. 1, pp. 5-5
Open Access | Times Cited: 1
Mechanical properties of cemented tailings and waste-rock backfill (CTWB) materials: Laboratory tests and deep learning modeling
Shenghua Yin, Zepeng Yan, Xun Chen, et al.
Construction and Building Materials (2023) Vol. 369, pp. 130610-130610
Closed Access | Times Cited: 23
Enhancing unconfined compressive strength prediction in nano-silica stabilized soil: a comparative analysis of ensemble and deep learning models
Ishwor Thapa, Sufyan Ghani
Modeling Earth Systems and Environment (2024) Vol. 10, Iss. 4, pp. 5079-5102
Closed Access | Times Cited: 10
Application of KNN-based isometric mapping and fuzzy c-means algorithm to predict short-term rockburst risk in deep underground projects
Muhammad Kamran, Barkat Ullah, Mahmood Ahmad, et al.
Frontiers in Public Health (2022) Vol. 10
Open Access | Times Cited: 36
Rock Strength Estimation Using Several Tree-Based ML Techniques
Zida Liu, Danial Jahed Armaghani, Pouyan Fakharian, et al.
Computer Modeling in Engineering & Sciences (2022) Vol. 133, Iss. 3, pp. 799-824
Open Access | Times Cited: 32
Prediction of Uniaxial Compressive Strength Using Fully Bayesian Gaussian Process Regression (fB-GPR) with Model Class Selection
Tengyuan Zhao, Chao Song, Shifeng Lu, et al.
Rock Mechanics and Rock Engineering (2022) Vol. 55, Iss. 10, pp. 6301-6319
Closed Access | Times Cited: 29
Rock strength prediction based on machine learning: A study from prediction model to mechanism explanation
Junlong Sun, Ru Zhang, Anlin Zhang, et al.
Measurement (2024) Vol. 238, pp. 115373-115373
Closed Access | Times Cited: 7
Characterizing marble strength and elasticity: Insights from destructive and non-destructive techniques on El Laurel formation (Ecuador)
Wilson F. Espinoza, Randy Moposita, Anthony Torres, et al.
Construction and Building Materials (2025) Vol. 468, pp. 140340-140340
Closed Access
Experimental Study and Soft Computing Modeling of the Unconfined Compressive Strength of Limestone Rocks Considering Dry and Saturation Conditions
Saif Alzabeebee, Diyari Abdalrahman Mohammed, Younis M. Alshkane
Rock Mechanics and Rock Engineering (2022) Vol. 55, Iss. 9, pp. 5535-5554
Closed Access | Times Cited: 24
Prediction of uniaxial compressive strength of rock based on lithology using stacking models
Zida Liu, Diyuan Li, Yongping Liu, et al.
Rock Mechanics Bulletin (2023) Vol. 2, Iss. 4, pp. 100081-100081
Open Access | Times Cited: 15
Enhancing Battery Pack Safety against Cone Impact Using Machine Learning Techniques and Gaussian Noise
Qian Zhang, Shaoyong Han, Azher M. Abed, et al.
Process Safety and Environmental Protection (2024) Vol. 191, pp. 448-465
Closed Access | Times Cited: 4
Risk assessment of mountain tunnel entrance collapse based on PSO-LSTM surface settlement prediction
Yazhen Sun, Kun Lin, Jinchang Wang, et al.
Engineering Construction & Architectural Management (2025)
Closed Access
Optimizing Schmidt Hammer Performance in Rock Testing: Integration of Kriging Surrogate Model and PSO-GWO Algorithm
Shenghao Piao, Sheng Huang, Jianhui Tan, et al.
Rock Mechanics and Rock Engineering (2025)
Closed Access
Hybrid catboost models optimized with metaheuristics for predicting shear strength in rock joints
Xiaohua Ding, Mahdi Hasanipanah, Mohammad Matin Rouhani, et al.
Bulletin of Engineering Geology and the Environment (2025) Vol. 84, Iss. 3
Closed Access
Assessment of mechanical properties of rock using deep learning approaches
Xiaohua Ding, Mahdi Hasanipanah, Mohammad Rezaei
Measurement (2025), pp. 117180-117180
Closed 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
Predicting load distribution in tie beam-foundation systems using machine learning and nature-inspired optimization algorithms
Ahmad S. Alfraihat
Asian Journal of Civil Engineering (2025)
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
Creep parameter inversion and long-term deformation prediction of a near-dam slope considering spatio-temporal deformation data during construction and impoundment period
Yaoru Liu, Wenyu Zhuang, Chao Gao, et al.
Engineering Geology (2025), pp. 108043-108043
Closed Access
Probabilistic prediction of uniaxial compressive strength for rocks from sparse data using Bayesian Gaussian process regression with Synthetic Minority Oversampling Technique (SMOTE)
Chao Song, Tengyuan Zhao, Xu Ling, et al.
Computers and Geotechnics (2023) Vol. 165, pp. 105850-105850
Open Access | Times Cited: 12
The Prediction of Transmission Towers’ Foundation Ground Subsidence in the Salt Lake Area Based on Multi-Temporal Interferometric Synthetic Aperture Radar and Deep Learning
Bijing Jin, Taorui Zeng, Taohui Yang, et al.
Remote Sensing (2023) Vol. 15, Iss. 19, pp. 4805-4805
Open Access | Times Cited: 11
Enhancing of uniaxial compressive strength of travertine rock prediction through machine learning and multivariate analysis
Dima A. Husein Malkawi, Samer R. Rabab’ah, Abdulla A. Sharo, et al.
Results in Engineering (2023) Vol. 20, pp. 101593-101593
Open Access | Times Cited: 10
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