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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:

Correlating the Unconfined Compressive Strength of Rock with the Compressional Wave Velocity Effective Porosity and Schmidt Hammer Rebound Number Using Artificial Neural Networks
Tien-Thinh Le, Athanasia D. Skentou, Anna Mamou, et al.
Rock Mechanics and Rock Engineering (2022) Vol. 55, Iss. 11, pp. 6805-6840
Closed Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

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: 54
Intelligent based decision-making strategy to predict fire intensity in subsurface engineering environments
Muhammad Kamran, Ridho Kresna Wattimena, Danial Jahed Armaghani, et al.
Process Safety and Environmental Protection (2023) Vol. 171, pp. 374-384
Closed Access | Times Cited: 35
Application of hybrid ANN paradigms built with nature inspired meta-heuristics for modelling soil compaction parameters
Abidhan Bardhan, Panagiotis G. Asteris
Transportation Geotechnics (2023) Vol. 41, pp. 100995-100995
Closed Access | Times Cited: 31
Assessment of Uniaxial Strength of Rocks: A Critical Comparison Between Evolutionary and Swarm Optimized Relevance Vector Machine Models
Jitendra Khatti, Kamaldeep Singh Grover
Transportation Infrastructure Geotechnology (2024) Vol. 11, Iss. 6, pp. 4098-4141
Closed Access | Times Cited: 10
A Novel Hybrid Bayesian-Group-Based Machine Learning (HB-GML) Method for Predicting Uniaxial Compressive Strength (UCS) of Rock
Shenghao Piao, Sheng Huang, Yingjie Wei, et al.
Rock Mechanics and Rock Engineering (2025)
Closed Access | Times Cited: 1
Revealing the nature of soil liquefaction using machine learning
Sufyan Ghani, Ishwor Thapa, Amrendra Kumar, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 2
Open Access | Times Cited: 1
Enhancing the accuracy of rebound test to predict rock UCS: reliability analysis and a novel wave impedance-based approach
Shenghao Piao, Sheng Huang, Yingjie Wei, et al.
Bulletin of Engineering Geology and the Environment (2025) Vol. 84, Iss. 2
Closed Access | Times Cited: 1
Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests
Yuzhen Wang, Mahdi Hasanipanah, Ahmad Safuan A. Rashid, et al.
Materials (2023) Vol. 16, Iss. 10, pp. 3731-3731
Open Access | Times Cited: 21
Robust Machine Learning Predictive Models for Real-Time Determination of Confined Compressive Strength of Rock Using Mudlogging Data
Milad Zamanzadeh Talkhouncheh, Shadfar Davoodi, David A. Wood, et al.
Rock Mechanics and Rock Engineering (2024) Vol. 57, Iss. 9, pp. 6881-6907
Closed Access | Times Cited: 6
Back analysis of rock mass parameters in tunnel engineering using machine learning techniques
Xiangyu Chang, Hao Wang, Yiming Zhang
Computers and Geotechnics (2023) Vol. 163, pp. 105738-105738
Closed Access | Times Cited: 16
Artificial intelligent techniques for prediction of rock strength and deformation properties – A review
Mujahid Ali, Sai Hin Lai
Structures (2023) Vol. 55, pp. 1542-1555
Closed Access | Times Cited: 15
Predicting uniaxial compressive strength of building stone based on index tests: Correlations, validity, reliability, and unification
Fanmeng Kong, Yiguo Xue, Junlong Shang, et al.
Construction and Building Materials (2024) Vol. 438, pp. 137227-137227
Closed Access | Times Cited: 5
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
Deep neural networks for the estimation of granite materials’ compressive strength using non-destructive indices
Danial Jahed Armaghani, Athanasia D. Skentou, Mehdi Izadpanah, et al.
Elsevier eBooks (2024), pp. 45-74
Closed Access | Times Cited: 4
Comparative study on convolutional neural network and regression analysis to evaluate uniaxial compressive strength of Sandy Dolomite
Meiqian Wang, Wenlian Liu, Haiming Liu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
Neural network coupled with nature-inspired optimization algorithms for prediction of microstructural and mechanical properties of nano-silica treated fine-grained soil
Ishwor Thapa, Sufyan Ghani, Panagiotis G. Asteris
Modeling Earth Systems and Environment (2025) Vol. 11, Iss. 1
Closed Access
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
Machine learning–based prediction of unconfined compressive strength and contaminant leachability in dredged contaminated sediments for land reclamation projects
Aamir Khan Mastoi, S Shahul Hameed, Mutahar Ali, et al.
Environmental Science and Pollution Research (2025)
Closed Access
A Comparative Study of UCS Results Obtained from Triaxial Tests Under Multiple Failure State Conditions (Test Type II)
Nghia Quoc Trinh, Eivind Grøv, Gunnar Vistnes
Applied Sciences (2025) Vol. 15, Iss. 6, pp. 3176-3176
Open Access
Classification of Rock Hardness at Tunnel Faces Based on a Drilling Parameter Cloud Map and Convolutional Neural Network
Mingnian Wang, Wenhao Yi, Qinyong Xia, et al.
Journal of Computing in Civil Engineering (2025) Vol. 39, Iss. 4
Closed Access
Bayesian selection of the optimal composite kernels for probabilistic estimation of UCS with explicit consideration of measurement errors and the anisotropic characteristic of input features
Tengyuan Zhao, Chao Song, Xu Ling, et al.
Georisk Assessment and Management of Risk for Engineered Systems and Geohazards (2025), pp. 1-23
Closed Access
Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data
Ayele Tesema Chala, Richard P. Ray
Applied Sciences (2023) Vol. 13, Iss. 14, pp. 8286-8286
Open Access | Times Cited: 10
Effect of intermittent joint distribution on the mechanical and acoustic behavior of rock masses
Shuaiyang Fu, Haibo Li, Liwang Liu, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2023) Vol. 16, Iss. 4, pp. 1231-1244
Open Access | Times Cited: 9
Implementation of a non-destructive method to assess weathering deterioration of sandstones in cultural heritage
Elisa Mammoliti, Marta Cupido, Riccardo Teloni, et al.
Bulletin of Engineering Geology and the Environment (2024) Vol. 83, Iss. 4
Open Access | Times Cited: 3
Machine Learning-Based Prediction of Shear Strength Parameters of Rock Materials
Dayong Han, Xinhua Xue
Rock Mechanics and Rock Engineering (2024) Vol. 57, Iss. 10, pp. 8795-8819
Closed Access | Times Cited: 3
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