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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:

Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms
Kaffayatullah Khan, Waqas Ahmad, Muhammad Nasir Amin, et al.
Polymers (2022) Vol. 14, Iss. 15, pp. 3065-3065
Open Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete
Seyed Soroush Pakzad, Naeim Roshan, Mansour Ghalehnovi
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 50
Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models
Mana Alyami, Roz‐Ud‐Din Nassar, Majid Khan, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02901-e02901
Open Access | Times Cited: 35
Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses
Abul Kashem, Rezaul Karim, Pobithra Das, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03030-e03030
Open Access | Times Cited: 28
Optimizing compressive strength prediction models for rice husk ash concrete with evolutionary machine intelligence techniques
Muhammad Nasir Amin, Waqas Ahmad, Kaffayatullah Khan, et al.
Case Studies in Construction Materials (2023) Vol. 18, pp. e02102-e02102
Open Access | Times Cited: 31
Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete
Mana Alyami, Majid Khan, Muhammad Faisal Javed, et al.
Developments in the Built Environment (2023) Vol. 17, pp. 100307-100307
Open Access | Times Cited: 27
A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin
Muhammad Iftikhar Faraz, Siyab Ul Arifeen, Muhammad Nasir Amin, et al.
Structures (2023) Vol. 53, pp. 937-948
Closed Access | Times Cited: 23
Machine and Deep Learning Methods for Concrete Strength Prediction: A Bibliometric and Content Analysis Review of Research Trends and Future Directions
Raman Kumar, Essam Althaqafi, S. Gopal Krishna Patro, et al.
Applied Soft Computing (2024) Vol. 164, pp. 111956-111956
Closed Access | Times Cited: 9
Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods
Hassan Ali Alkadhim, Muhammad Nasir Amin, Waqas Ahmad, et al.
Materials (2022) Vol. 15, Iss. 20, pp. 7344-7344
Open Access | Times Cited: 34
Development of the New Prediction Models for the Compressive Strength of Nanomodified Concrete Using Novel Machine Learning Techniques
Sohaib Nazar, Jian Yang, Waqas Ahmad, et al.
Buildings (2022) Vol. 12, Iss. 12, pp. 2160-2160
Open Access | Times Cited: 34
Predicting the crack width of the engineered cementitious materials via standard machine learning algorithms
Xiongzhou Yuan, Qingyu Cao, Muhammad Nasir Amin, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 6187-6200
Open Access | Times Cited: 16
Data-driven approaches for strength prediction of alkali-activated composites
Mohammed Awad Abuhussain, Ayaz Ahmad, Muhammad Nasir Amin, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02920-e02920
Open Access | Times Cited: 3
Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma
Lizhen Xu, Liangchun Cai, Zheng Zhu, et al.
BMC Endocrine Disorders (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 9
In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches
Abdulrahman Mohamad Radwan Bulbul, Kaffayatullah Khan, Afnan Nafees, et al.
Materials (2022) Vol. 15, Iss. 21, pp. 7764-7764
Open Access | Times Cited: 14
Research on dynamic constitutive model of steel fiber reinforced concrete with different steel fiber content and matrix strength
Zhange Bi, Jun Liu, Futian Zhao, et al.
Construction and Building Materials (2024) Vol. 433, pp. 136671-136671
Closed Access | Times Cited: 2
Machine-learning networks to predict the ultimate axial load and displacement capacity of 3D printed concrete walls with different section geometries
İffet Gamze Mütevelli Özkan, Alper Aldemir
Structures (2024) Vol. 66, pp. 106879-106879
Closed Access | Times Cited: 2
Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates
Saeid Saberi, Hamid Nasiri, Omid Ghorbani, et al.
Materials (2023) Vol. 16, Iss. 15, pp. 5381-5381
Open Access | Times Cited: 6
Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica
Kaffayatullah Khan, Muhammad Nasir Amin, Umbreen Us Sahar, et al.
Frontiers in Materials (2022) Vol. 9
Open Access | Times Cited: 10
Machine Learning for Predicting Stillbirth: A Systematic Review
Qingyuan Li, Pan Li, Junyu Chen, et al.
Reproductive Sciences (2024)
Closed Access | Times Cited: 1
Advancements in Soil Quality Assessment: A Comprehensive Review of Machine Learning and AI-Driven Approaches for Nutrient Deficiency Analysis
S. Barathkumar, K. M. Sellamuthu, K. Sathyabama, et al.
Communications in Soil Science and Plant Analysis (2024) Vol. 56, Iss. 2, pp. 251-276
Closed Access | Times Cited: 1
Machine learning models for predicting depression in Korean young employees
Suk‐Sun Kim, Minji Gil, Eun Jeong Min
Frontiers in Public Health (2023) Vol. 11
Open Access | Times Cited: 4
Bio-inspired based meta-heuristic approach for predicting the strength of fiber-reinforced based strain hardening cementitious composites
Y. A. Khan, Adeel Zafar, Muhammad Faisal Rehman, et al.
Heliyon (2023) Vol. 9, Iss. 11, pp. e21601-e21601
Open Access | Times Cited: 3
Investigating compressive strength of concrete containing steel fiber by data-driven approach
Tran Van Quan, Nguyễn Ngọc Linh, Nguyễn Ngọc Tân
Tạp chí Khoa học Công nghệ Xây dựng (KHCNXD) - ĐHXDHN (2023) Vol. 17, Iss. 3, pp. 65-79
Open Access | Times Cited: 2
THE EFFECT OF INDUSTRIAL AND WASTE FIBERS ON CONCRETE STRENGTH AND STRUCTURAL BEHAVIOR OF RC SHORT COLUMNS
Maryam H. Naser, Mayadah W. Falah, Fatimah H. Naser, et al.
IIUM Engineering Journal (2024) Vol. 25, Iss. 1, pp. 87-101
Open Access
Comparison of Cox Regression to Machine Learning in Predicting Cancer-Specific Survival of Fibroblastic Osteosarcoma
Longteng Chao, Xinmiao Ye, Junyuan Chen, et al.
Research Square (Research Square) (2024)
Open Access
Behaviour of steel-fiber reinforced concrete at elevated temperatures
D. Golani, Sanjiv Mohanty, Niranjan Yadav
IOP Conference Series Earth and Environmental Science (2024) Vol. 1326, Iss. 1, pp. 012151-012151
Open Access
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