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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:

Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment
Noor Md. Sadiqul Hasan, Md. Habibur Rahman Sobuz, Nur Mohammad Nazmus Shaurdho, et al.
Heliyon (2023) Vol. 9, Iss. 11, pp. e22296-e22296
Open Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

Assessing the influence of sugarcane bagasse ash for the production of eco-friendly concrete: Experimental and machine learning approaches
Md. Habibur Rahman Sobuz, Al-Imran, Shuvo Dip Datta, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02839-e02839
Open Access | Times Cited: 53
Optimization of recycled rubber self-compacting concrete: Experimental findings and machine learning-based evaluation
Md. Habibur Rahman Sobuz, Limon Paul Joy, Abu Sayed Mohammad Akid, et al.
Heliyon (2024) Vol. 10, Iss. 6, pp. e27793-e27793
Open Access | Times Cited: 31
Analysis of the characteristics and environmental benefits of rice husk ash as a supplementary cementitious material through experimental and machine learning approaches
Shuvo Dip Datta, Md. Mamun Sarkar, Arifa Sultana Rakhe, et al.
Innovative Infrastructure Solutions (2024) Vol. 9, Iss. 4
Closed Access | Times Cited: 25
Assessing the Engineering Properties and Environmental Impact with Explainable Machine Learning analysis of Sustainable Concrete Utilizing Waste Banana Leaf Ash as a Partial Cement Replacement
Asif Mahmud Momshad, Md. Hamidul Islam, Shuvo Dip Datta, et al.
Cleaner Engineering and Technology (2025), pp. 100886-100886
Open Access | Times Cited: 10
Experimental Investigation on Fresh, Hardened and Durability Characteristics of Partially Replaced E-Waste Plastic Concrete: A Sustainable Concept with Machine Learning Approaches
Md. Hamidul Islam, Zannatun Noor Prova, Md. Habibur Rahman Sobuz, et al.
Heliyon (2025) Vol. 11, Iss. 2, pp. e41924-e41924
Open Access | Times Cited: 3
AI-driven Modeling for the Optimization of Concrete Strength for Low-Cost Business Production in the USA Construction Industry
Md. Habibur Rahman Sobuz, Md. Abu Saleh, Md. Samiun, et al.
Engineering Technology & Applied Science Research (2025) Vol. 15, Iss. 1, pp. 20529-20537
Open Access | Times Cited: 2
Predictive models in machine learning for strength and life cycle assessment of concrete structures
A. Dinesh, B. Rahul Prasad
Automation in Construction (2024) Vol. 162, pp. 105412-105412
Closed Access | Times Cited: 15
Comparative study of different machine learning approaches for predicting the compressive strength of palm fuel ash concrete
Yasmina Kellouche, Bassam A. Tayeh, Yazid Chetbani, et al.
Journal of Building Engineering (2024) Vol. 88, pp. 109187-109187
Closed Access | Times Cited: 10
Ultra-light foamed concrete mechanical properties and thermal insulation perspective: A comprehensive review
Abdeliazim Mustafa Mohamed, Bassam A. Tayeh, Samadar S. Majeed, et al.
Journal of CO2 Utilization (2024) Vol. 83, pp. 102827-102827
Open Access | Times Cited: 10
Risk Analysis-based Decision Support System for Designing Cybersecurity of Information Technology
Barna Biswas, Sadia Sharmin, Md. Azad Hossain, et al.
Journal of Business and Management Studies (2024) Vol. 6, Iss. 5, pp. 13-22
Open Access | Times Cited: 10
Artificial intelligence on the agro-industry in the United States of America
Jahanara Akter, Sadia Islam Nilima, Rakibul Hasan, et al.
AIMS Agriculture and Food (2024) Vol. 9, Iss. 4, pp. 959-979
Open Access | Times Cited: 8
Assessment of Hybrid Fiber Reinforced Graphene Nano-Engineered Concrete Composites: From Experimental Testing to Explainable Machine Learning Modeling
Md. Habibur Rahman Sobuz, Rahat Aayaz, SM Arifur Rahman, et al.
Journal of Materials Research and Technology (2025)
Open Access | Times Cited: 1
Experimental assessment and hybrid machine learning-based feature importance analysis with the optimization of compressive strength of waste glass powder-modified concrete
Turki S. Alahmari, Md. Kawsarul Islam Kabbo, Md. Habibur Rahman Sobuz, et al.
Materials Today Communications (2025), pp. 112081-112081
Closed Access | Times Cited: 1
Behavioral Intention to Adopt Artificial Intelligence in Educational Institutions: A Hybrid Modeling Approach
Ashok Ghimire, Md Ahsan Ullah Imran, Barna Biswas, et al.
Journal of Computer Science and Technology Studies (2024) Vol. 6, Iss. 3, pp. 56-64
Open Access | Times Cited: 7
An explainable machine learning model for encompassing the mechanical strength of polymer-modified concrete
Md. Habibur Rahman Sobuz, M.R. Khatun, Md. Kawsarul Islam Kabbo, et al.
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 6
Machine learning algorithms based approach on prediction of mechanical behaviour of PLA/brass composites manufactured by additive manufacturing
Nisha Soms, K. Ravikumar, M Naveen Kumar
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (2025)
Closed Access
Synergistic effect of waste gypsum plasterboard and fly ash as partial cement replacement on fresh-state, microstructural, mechanical and transport properties of foamed concrete
Ahmed M. Maglad, Md Azree Othuman Mydin, Cyriaque Rodrigue Kaze, et al.
Construction and Building Materials (2025) Vol. 463, pp. 140079-140079
Closed Access
Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis
Tariq Ali, Kennedy C. Onyelowe, Muhammad Sarmad Mahmood, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Analyzing the compressive strength, eco-strength, and cost–strength ratio of agro-waste-derived concrete using advanced machine learning methods
Muhammad Nasir Amin, Bawar Iftikhar, Kaffayatullah Khan, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2025) Vol. 64, Iss. 1
Open Access
Characterization of the Pozzolanic Potential of Oil Palm Kernel Shell Ash Obtained Through Optimization of Physicochemical Processes
Ramon Torres Ortega, Mario Velasco, Jair de Jesús Arrieta Baldovino
Materials (2025) Vol. 18, Iss. 6, pp. 1248-1248
Open Access
How machine learning can transform the future of concrete
Kaoutar Mouzoun, Azzeddine Bouyahyaoui, Hanane Moulay Abdelali, et al.
Asian Journal of Civil Engineering (2025)
Closed Access
Microstructural assessment and supervised machine learning-aided modeling to explore the potential of quartz powder as an alternate binding material in concrete
Md. Habibur Rahman Sobuz, Md. Kawsarul Islam Kabbo, M.R. Khatun, et al.
Case Studies in Construction Materials (2025), pp. e04568-e04568
Open Access
Modeling the compressive strength behavior of concrete reinforced with basalt fiber
Kennedy C. Onyelowe, Ahmed M. Ebid, Shadi Hanandeh, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Experimental Assessment and Machine Learning Quantification of Structural Eco-Cellular Lightweight Concrete Incorporating Waste Marble Powder and Silica Fume
Md. Kawsarul Islam Kabbo, Md. Habibur Rahman Sobuz, Fahim Shahriyar Aditto, et al.
Journal of Building Engineering (2025), pp. 112557-112557
Closed Access
Assessment of bonding strength of steel bar in recycled aggregate concrete containing ground palm oil fuel ash
Thanawich Sripan, Sattawat Haruehansapong, Wunchock Kroehong, et al.
Innovative Infrastructure Solutions (2024) Vol. 9, Iss. 3
Closed Access | Times Cited: 3
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