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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:

Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis
Hai‐Van Thi, May Huu Nguyen, Hai‐Bang Ly
Construction and Building Materials (2023) Vol. 367, pp. 130339-130339
Closed Access | Times Cited: 62

Showing 1-25 of 62 citing articles:

Efficient machine learning method for evaluating compressive strength of cement stabilized soft soil
Chen Zhang, Zhiduo Zhu, Fa Liu, et al.
Construction and Building Materials (2023) Vol. 392, pp. 131887-131887
Closed Access | Times Cited: 42
Prediction of high strength ternary blended concrete containing different silica proportions using machine learning approaches
T. Vamsi Nagaraju, Sireesha Mantena, Marc Azab, et al.
Results in Engineering (2023) Vol. 17, pp. 100973-100973
Open Access | Times Cited: 40
Prediction of compressive strength of high-volume fly ash self-compacting concrete with silica fume using machine learning techniques
Shashikant Kumar, Rakesh Kumar, Baboo Rai, et al.
Construction and Building Materials (2024) Vol. 438, pp. 136933-136933
Closed Access | Times Cited: 17
Toward improved prediction of recycled brick aggregate concrete compressive strength by designing ensemble machine learning models
Hai‐Van Thi, May Huu Nguyen, Son Hoang Trinh, et al.
Construction and Building Materials (2023) Vol. 369, pp. 130613-130613
Closed Access | Times Cited: 38
Prediction of mechanical properties of recycled aggregate fly ash concrete employing machine learning algorithms
Maedeh Hosseinzadeh, Mehdi Dehestani, Alireza Hosseinzadeh
Journal of Building Engineering (2023) Vol. 76, pp. 107006-107006
Closed Access | Times Cited: 35
Eco-friendly 3D printed concrete with fine aggregate replacements: Fabrication, characterization and machine learning prediction
Ronghua Zhu, King-James Idala Egbe, Hadi Salehi, et al.
Construction and Building Materials (2024) Vol. 413, pp. 134905-134905
Closed Access | Times Cited: 12
Prediction of non-uniform shrinkage of steel-concrete composite slabs based on explainable ensemble machine learning model
Shiqi Wang, Jinlong Liu, Qinghe Wang, et al.
Journal of Building Engineering (2024) Vol. 88, pp. 109002-109002
Closed Access | Times Cited: 11
Hybrid XGB model for predicting unconfined compressive strength of solid waste-cement-stabilized cohesive soil
Qianglong Yao, Yiliang Tu, Jiahui Yang, et al.
Construction and Building Materials (2024) Vol. 449, pp. 138242-138242
Closed Access | Times Cited: 9
An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete
D.P.P. Meddage, Isuri Fonseka, Damith Mohotti, et al.
Construction and Building Materials (2024) Vol. 449, pp. 138346-138346
Open Access | Times Cited: 9
Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend
Ramin Kazemi
Engineering Reports (2023) Vol. 5, Iss. 9
Open Access | Times Cited: 19
Evaluating machine learning algorithms for predicting compressive strength of concrete with mineral admixture using long short-term memory (LSTM) Technique
Abhilash Gogineni, Mrutyunjay Rout, Kumar Shubham
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 2, pp. 1921-1933
Closed Access | Times Cited: 16
XGB-Northern Goshawk Optimization: Predicting the Compressive Strength of Self-Compacting Concrete
Jiang Bian, Ruili Huo, Yan Zhong, et al.
KSCE Journal of Civil Engineering (2024) Vol. 28, Iss. 4, pp. 1423-1439
Open Access | Times Cited: 6
Enhancing high-strength self-compacting concrete properties through Nano-silica: analysis and prediction of mechanical strengths
Md. Faiz Alam, Kumar Shubham, Sanjay Kumar, et al.
Journal of Building Pathology and Rehabilitation (2024) Vol. 9, Iss. 1
Closed Access | Times Cited: 6
The impact of different length hooked‐end fibers on the structural performance of RC folded plates
Metin Katlav, Kâzım Türk, Paki Turğut
Structural Concrete (2024)
Open Access | Times Cited: 6
Optimizing machine learning techniques and SHapley Additive exPlanations (SHAP) analysis for the compressive property of self-compacting concrete
Zhiyuan Wang, Huihui Liu, Muhammad Nasir Amin, et al.
Materials Today Communications (2024) Vol. 39, pp. 108804-108804
Closed Access | Times Cited: 5
A novel compressive strength estimation approach for 3D printed fiber-reinforced concrete: integrating machine learning and gene expression programming
Md Nasir Uddin, Junhong Ye, M. Aminul Haque, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 5, pp. 4889-4910
Closed Access | Times Cited: 5
Transfer learning-based confinement strength prediction of concrete confined by FRP transverse reinforcements
Fei Peng, Yazhong Li, Weichen Xue
Engineering Structures (2024) Vol. 310, pp. 118116-118116
Closed Access | Times Cited: 5
Effect of plastic fibers on OPC-based concrete in circular economy
Shehroze Ali, Suliman Khan, Muhammad Imran, et al.
Innovative Infrastructure Solutions (2025) Vol. 10, Iss. 2
Closed Access
Improving flood-prone areas mapping using geospatial artificial intelligence (GeoAI): A non-parametric algorithm enhanced by math-based metaheuristic algorithms
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Farman Ali, et al.
Journal of Environmental Management (2025) Vol. 375, pp. 124238-124238
Closed Access
Optimizing high-strength concrete compressive strength with explainable machine learning
Sanjog Chhetri Sapkota, Christina Panagiotakopoulou, Dipak Dahal, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
Open Access
Predicting the compressive strength of solid waste-cement stabilized compacted soil using machine learning model
Qianglong Yao, Yiliang Tu, Jiahui Yang
Materials Today Communications (2025), pp. 111882-111882
Closed Access
Explainable artificial intelligence-based compressive strength optimization and Life-Cycle Assessment of eco-friendly sugarcane bagasse ash concrete
Varisha Rizwan, Syed Muhammad Ibrahim, Mohd Moonis Zaheer, et al.
Environmental Science and Pollution Research (2025)
Closed Access
Towards sustainable construction: estimating compressive strength of waste foundry sand-blended green concrete using a hybrid machine learning approach
Nhat‐Duc Hoang, Nguyen Quoc-Lam
Deleted Journal (2025) Vol. 2, Iss. 1
Open Access
Machine learning–based prediction of unconfined compressive strength and contaminant leachability in dredged contaminated sediments for land reclamation projects
Aamir Khan Mastoi, S Shahul Hameed, Mutahar Ali, et al.
Environmental Science and Pollution Research (2025)
Closed Access
Machine Learning Approaches for Predicting Mechanical Properties of Steel-Fiber-Reinforced Concrete
Huan Huang, Long Lei, Guoxin Xu, et al.
Materials Today Communications (2025), pp. 112149-112149
Closed Access
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