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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:

Artificial intelligence forecasting models of uniaxial compressive strength
Arsalan Mahmoodzadeh, Mokhtar Mohammadi, Hawkar Hashim Ibrahim, et al.
Transportation Geotechnics (2020) Vol. 27, pp. 100499-100499
Closed Access | Times Cited: 83

Showing 1-25 of 83 citing articles:

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
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
Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
R.S.S. Ranasinghe, W.K.V.J.B. Kulasooriya, Udara Sachinthana Perera, et al.
Results in Engineering (2024) Vol. 23, pp. 102503-102503
Open Access | Times Cited: 15
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
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
Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques
Arsalan Mahmoodzadeh, Mokhtar Mohammadi, Krikar M-Gharrib Noori, et al.
Automation in Construction (2021) Vol. 127, pp. 103719-103719
Closed Access | Times Cited: 64
Prediction of safety factors for slope stability: comparison of machine learning techniques
Arsalan Mahmoodzadeh, Mokhtar Mohammadi, Hunar Farid Hama Ali, et al.
Natural Hazards (2021) Vol. 111, Iss. 2, pp. 1771-1799
Closed Access | Times Cited: 58
Machine learning forecasting models of disc cutters life of tunnel boring machine
Arsalan Mahmoodzadeh, Mokhtar Mohammadi, Hawkar Hashim Ibrahim, et al.
Automation in Construction (2021) Vol. 128, pp. 103779-103779
Closed Access | Times Cited: 56
Optimized machine learning modelling for predicting the construction cost and duration of tunnelling projects
Arsalan Mahmoodzadeh, Hamid Reza Nejati, Mokhtar Mohammadi
Automation in Construction (2022) Vol. 139, pp. 104305-104305
Closed Access | Times Cited: 45
Classification of surface settlement levels induced by TBM driving in urban areas using random forest with data-driven feature selection
Dongku Kim, Khanh Pham, Ju-Young Oh, et al.
Automation in Construction (2022) Vol. 135, pp. 104109-104109
Closed Access | Times Cited: 44
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: 13
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
Predicting grout’s uniaxial compressive strength (UCS) for fully grouted rock bolting system by applying ensemble machine learning techniques
Shahab Hosseini, Shima Entezam, Behshad Jodeiri Shokri, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 29, pp. 18387-18412
Open Access | Times Cited: 9
Estimating unconfined compressive strength of unsaturated cemented soils using alternative evolutionary approaches
Navid Kardani, Annan Zhou, Shui‐Long Shen, et al.
Transportation Geotechnics (2021) Vol. 29, pp. 100591-100591
Closed Access | Times Cited: 46
Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using an explainable artificial intelligence
Hamid Nasiri, Arman Homafar, Saeed Chehreh Chelgani
Results in Geophysical Sciences (2021) Vol. 8, pp. 100034-100034
Open Access | Times Cited: 43
Evaluation and prediction of the rock static and dynamic parameters
Marzieh Khosravi, Somayeh Tabasi, Hany Hossam Eldien, et al.
Journal of Applied Geophysics (2022) Vol. 199, pp. 104581-104581
Closed Access | Times Cited: 35
Design of concrete incorporating microencapsulated phase change materials for clean energy: A ternary machine learning approach based on generative adversarial networks
Afshin Marani, Lei Zhang, Moncef L. Nehdi
Engineering Applications of Artificial Intelligence (2022) Vol. 118, pp. 105652-105652
Closed Access | Times Cited: 35
A Kernel Extreme Learning Machine-Grey Wolf Optimizer (KELM-GWO) Model to Predict Uniaxial Compressive Strength of Rock
Chuanqi Li, Jian Zhou, Daniel Dias, et al.
Applied Sciences (2022) Vol. 12, Iss. 17, pp. 8468-8468
Open Access | Times Cited: 33
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
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: 19
Prediction of the Uniaxial Compressive Strength of Rocks by Soft Computing Approaches
Reza Khajevand
Geotechnical and Geological Engineering (2023) Vol. 41, Iss. 6, pp. 3549-3574
Closed Access | Times Cited: 18
Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms
Junjie Zhao, Diyuan Li, Jingtai Jiang, et al.
Computer Modeling in Engineering & Sciences (2024) Vol. 140, Iss. 1, pp. 275-304
Open Access | Times Cited: 7
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
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
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