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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:

Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming
Bawar Iftikhar, Sophia C. Alih, Mohammadreza Vafaei, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

Metaheuristic optimization of machine learning models for strength prediction of high-performance self-compacting alkali-activated slag concrete
Suraj Kumar Parhi, Soumyaranjan Panda, Saswat Dwibedy, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 3, pp. 2901-2928
Closed Access | Times Cited: 16
Multi-expression programming based prediction of the seismic capacity of reinforced concrete rectangular columns
Raheel Asghar, Muhammad Faisal Javed, Muhammad Saqib, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107834-107834
Closed Access | Times Cited: 11
RETRACTED: Analytical review on potential use of waste engine oil in asphalt and pavement engineering
Zahraa Jwaida, Anmar Dulaimi, Alireza Bahrami, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02930-e02930
Open Access | Times Cited: 8
Soft computing techniques for predicting the properties of raw rice husk concrete bricks using regression-based machine learning approaches
Nakkeeran Ganasen, L. Krishnaraj, Kennedy C. Onyelowe, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 19
Developing a model for waste plastic biofuels in CRDi diesel engines using FTIR, GCMS, and WASPAS synchronisations for engine analysis
Sumit Kanchan, Swastik Pradhan, Rajeev Kumar, et al.
Energy Exploration & Exploitation (2023) Vol. 42, Iss. 2, pp. 648-684
Open Access | Times Cited: 18
A comparative study of prediction models for alkali-activated materials to promote quick and economical adaptability in the building sector
Siyab Ul Arifeen, Muhammad Nasir Amin, Waqas Ahmad, et al.
Construction and Building Materials (2023) Vol. 407, pp. 133485-133485
Closed Access | Times Cited: 17
Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite
Xiqiao Xia
PLoS ONE (2024) Vol. 19, Iss. 1, pp. e0296494-e0296494
Open Access | Times Cited: 6
Exploring the influence of waste glass granular replacement on compressive strength in concrete mixtures: a normalization and modeling study
Soran Abdrahman Ahmad, Hemn Unis Ahmed, Serwan Rafiq, et al.
Journal of Building Pathology and Rehabilitation (2024) Vol. 9, Iss. 1
Open Access | Times Cited: 5
Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches
Muhammad Faisal Javed, Muhammad Fawad, Rida Hameed Lodhi, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Ductility and strength of reinforced concrete beams strengthened with aluminum CNC waste
Ibrahim Almeshal, Yasin Onuralp Özkılıç, Ceyhun Aksoylu, et al.
Structural Concrete (2024) Vol. 25, Iss. 5, pp. 3232-3245
Closed Access | Times Cited: 5
Supplementary cementitious materials-based concrete porosity estimation using modeling approaches: A comparative study of GEP and MEP
Qiong Tian, Yijun Lü, Ji Zhou, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Closed Access | Times Cited: 5
Optimized decision tree algorithms to estimate ultimate strain of concrete wrapped by aramid fiber-reinforced polymer
Yangyang Guo
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 4
Closed Access
Tensile behavior evaluation of two-stage concrete using an innovative model optimization approach
Muhammad Nasir Amin, Faizullah Jan, Kaffayatullah Khan, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2025) Vol. 64, Iss. 1
Open Access
Artificial intelligence-based optimized models for predicting the slump and compressive strength of sustainable alkali-derived concrete
Baoping Zou, Yanbing Wang, Muhammad Nasir Amin, et al.
Construction and Building Materials (2023) Vol. 409, pp. 134092-134092
Closed Access | Times Cited: 15
Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest
Hisham Jahangir Qureshi, Mana Alyami, Rab Nawaz, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02581-e02581
Open Access | Times Cited: 13
Toward sustainability: Integrating experimental study and data-driven modeling for eco-friendly paver blocks containing plastic waste
Usama Asif, Muhammad Faisal Javed, Deema Mohammed Alsekait, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 4
Experimental investigation of mechanical and durability performances of self-compacting concrete blended with bagasse ash, metakaolin, and glass fiber
Monali Wagh, Uday Waghe, Alireza Bahrami, et al.
Frontiers in Materials (2024) Vol. 11
Open Access | Times Cited: 4
A Review of the implementation of R-imperatives in circular construction
Jegen Pauline, Gast Lukas, F. E. Martin
Cleaner Production Letters (2025), pp. 100097-100097
Open Access
An Explainable Machine Learning (XML) approach to determine strength of glass powder concrete
Wali Ullah Khan, Waleed Bin Inqiad, Bilal Ayub, et al.
Materials Today Communications (2025), pp. 112181-112181
Closed Access
Hyper-tuning gene expression programming to develop interpretable prediction models for the strength of corncob ash-modified geopolymer concrete
Ji Zhou, Qiong Tian, Sohaib Nazar, et al.
Materials Today Communications (2023) Vol. 38, pp. 107885-107885
Closed Access | Times Cited: 11
Strength evaluation of eco-friendly waste-derived self-compacting concrete via interpretable genetic-based machine learning models
Zhiqiang Chen, Bawar Iftikhar, Ayaz Ahmad, et al.
Materials Today Communications (2023) Vol. 37, pp. 107356-107356
Closed Access | Times Cited: 10
Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model
Pravin R. Kshirsagar, Kamal Upreti, Virendra Singh Kushwah, et al.
Signal Image and Video Processing (2024) Vol. 18, Iss. S1, pp. 183-197
Closed Access | Times Cited: 3
Analyzing the efficacy of waste marble and glass powder for the compressive strength of self-compacting concrete using machine learning strategies
Qing Guan, Zhong Ling Tong, Muhammad Nasir Amin, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 3
Multi-objective statistical optimisation utilising response surface methodology to predict engine performance using biofuels from waste plastic oil in CRDi engines
Sumit Kanchan, Manisha Priyadarshini, Prem Kumar, et al.
Green Processing and Synthesis (2024) Vol. 13, Iss. 1
Open Access | Times Cited: 3
Data-driven strategy for evaluating the response of eco-friendly concrete at elevated temperatures for fire resistance construction
Fahad Alsharari, Bawar Iftikhar, Md. Alhaz Uddin, et al.
Results in Engineering (2023) Vol. 20, pp. 101595-101595
Open Access | Times Cited: 7
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