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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:

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: 18

Showing 18 citing articles:

Assessment of the uniaxial compressive strength of intact rocks: an extended comparison between machine and advanced machine learning models
Jitendra Khatti, Kamaldeep Singh Grover
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3301-3325
Closed Access | Times Cited: 17
Automated machine learning techniques for estimating the unconfined compressive strength of soil stabilization
Lei Wang
International Journal on Interactive Design and Manufacturing (IJIDeM) (2025)
Closed Access | Times Cited: 1
Prediction of Uniaxial Strength of Rocks Using Relevance Vector Machine Improved with Dual Kernels and Metaheuristic Algorithms
Jitendra Khatti, Kamaldeep Singh Grover
Rock Mechanics and Rock Engineering (2024) Vol. 57, Iss. 8, pp. 6227-6258
Closed Access | Times Cited: 12
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: 9
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
Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning
Jitendra Khatti, Kamaldeep Singh Grover
Transportation Infrastructure Geotechnology (2023) Vol. 11, Iss. 4, pp. 1989-2022
Closed Access | Times Cited: 18
Determining equivalent rectangular stress block parameters in geopolymer concrete using multi-gene genetic algorithm-supported models and ML techniques
Ahmet Özbayrak
Structures (2025) Vol. 73, pp. 108323-108323
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
Enhanced slope stability prediction using ensemble machine learning techniques
Devendra Kumar Yadav, Swarup Chattopadhyay, Debi Prasad Tripathy, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Optimized Gradient Boosting Models for Adaptive Prediction of Uniaxial Compressive Strength in Carbonate Rocks Using Drilling Data
Stephen Adjei, Ahmed Gowida, Salaheldin Elkatatny, et al.
ACS Omega (2025)
Open Access
Prediction of slope stability based on five machine learning techniques approaches: a comparative study
Soe Hlaing Tun, Chusheng Zeng, F Guimaraes Silvio Jamil
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 5
Closed Access
Comprehensive study on the Python-based regression machine learning models for prediction of uniaxial compressive strength using multiple parameters in Charnockite rocks
Sowmya Kochukrishnan, K. Premalatha, D. Yuvarajan, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
Machine Learning in Reservoir Engineering: A Review
Wensheng Zhou, Chen Liu, Yuandong Liu, et al.
Processes (2024) Vol. 12, Iss. 6, pp. 1219-1219
Open Access | Times Cited: 4
Hybrid machine learning approach for accurate prediction of the drilling rock index
Niaz Muhammad Shahani, Xigui Zheng, Xin Wei, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Developing some models to predict the uniaxial compressive strength of various sedimentary rocks (Case studies: large dam site and mine in Southeast China)
Zhe Wang, Zhou Zhou, Tao Sun, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03817-e03817
Open Access | Times Cited: 1
Optimizing Stabilization of Contaminated Mining Sludge: A Machine Learning Approach to Predict Strength and Heavy Metal Leaching
Traore Abdoul Fatah, Aamir Khan Mastoi, Nadeem-ul-Karim Bhatti, et al.
Arabian Journal for Science and Engineering (2024)
Closed Access | Times Cited: 1
Enhancing Unconfined Compressive Strength prediction in geotechnical engineering: a novel approach using LSZO and LSWG optimization
Kunpeng Zhao, Guanglei Zhao
Smart Science (2024), pp. 1-19
Closed Access
Visualising the strength development of FICP-treated sand using impedance spectroscopy
Jamal Ahmad, Mohammad Arsalan Khan, Shakeel Ahmad, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Estimation of Uniaxial Strength of Rock: A Comparison between Bayesian-Optimized Machine Learning Models
Jitendra Khatti, Kamaldeep Singh Grover
Mining Metallurgy & Exploration (2024)
Closed Access
Predicting the unconfined compressive strength of stabilized soil using random forest coupled with meta-heuristic algorithms
Yanjun Li
Journal of Ambient Intelligence and Humanized Computing (2024)
Closed Access
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