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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 the compressive strength of UHPC with coarse aggregates in the context of machine learning
Yan Yuan, Ming Yang, Xiangwen Shang, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02627-e02627
Open Access | Times Cited: 16

Showing 16 citing articles:

Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses
Abul Kashem, Rezaul Karim, Somir Chandra Malo, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02991-e02991
Open Access | Times Cited: 58
Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures
Hany A. Dahish, Ahmed D. Almutairi
Results in Engineering (2025), pp. 103975-103975
Open Access | Times Cited: 5
Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete
Mohamed Abdellatief, G. Murali, Saurav Dixit
Results in Engineering (2025) Vol. 25, pp. 104542-104542
Open Access | Times Cited: 3
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 | Times Cited: 2
Data-Driven Prediction of Compressive Strength for Ultra-high Performance Concrete Exposed to Elevated Temperatures
Xiaoqing Yu, Canhui Chen, Jianliang Xie, et al.
Materials Today Communications (2025), pp. 111518-111518
Closed Access | Times Cited: 1
Prediction Models for the Hybrid Effect of Nano Materials on Radiation Shielding Properties of Concrete Exposed to Elevated Temperatures
Mohammed K. Alkharisi, Hany A. Dahish, Osama Youssf
Case Studies in Construction Materials (2024) Vol. 21, pp. e03750-e03750
Open Access | Times Cited: 7
Simulation and modeling of polymer concrete panels using deep neural networks
Li Li, Mina Mortazavi, Harry Far, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02912-e02912
Open Access | Times Cited: 5
Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning
Hao Ji, Wenbin Jiao, Xie Xu, et al.
Case Studies in Construction Materials (2025) Vol. 22, pp. e04255-e04255
Open Access
Application of machine learning and optimization-based mesoscopic parameter inversion in numerical models of cement-stabilized macadam
Chang‐Chun Ling, Chen Chen, Xin Yu, et al.
International Journal of Pavement Engineering (2025) Vol. 26, Iss. 1
Closed Access
Optimizing NGBoost with Dynamic Sequential Model-Based Optimization for Predicting UHPC Compressive Strength on Heterogeneous Datasets
Taimur Rahman, Md. Farhad Momin, S. Podder, et al.
Materials Today Communications (2025), pp. 112405-112405
Closed Access
Assessment of compressive strength of ultra‐high‐performance concrete using advanced machine learning models
Ahmadullah Tabani, Rahul Biswas
Structural Concrete (2025)
Closed Access
Experimental testing and machine learning to predict the load-slip behavior of stud connectors in steel-UHPC composite structures
Yongjian Zhou, Yang Xia, Wei Zou, et al.
Engineering Structures (2025) Vol. 335, pp. 120418-120418
Closed Access
Investigation of Mechanical and Fresh Properties of Ultra-High-Performance Concrete Incorporating Second-Generation Superplasticizers
Sama Tajasosi, Jalil Barandoust, Ashkan Saradar, et al.
Applied Sciences (2025) Vol. 15, Iss. 9, pp. 5133-5133
Open Access
Mix proportion design based on particle compact packing theory and research on the resistance of metakaolin to chloride salt erosion in UHPC cementitious system
Hong Fen, Shuang Yu, Wangzhe Sun, et al.
Construction and Building Materials (2024) Vol. 447, pp. 137982-137982
Closed Access | Times Cited: 2
AI-infused characteristics prediction and multi-objective design of ultra-high performance concrete (UHPC): From pore structures to macro-performance
Wangyang Xu, Lingyan Zhang, Dingqiang Fan, et al.
Journal of Building Engineering (2024), pp. 111170-111170
Closed Access | Times Cited: 2
Using Multiple Machine Learning Models to Predict the Strength of UHPC Mixes with Various FA Percentages
Hussam Safieh, Rami A. Hawileh, Maha Assad, et al.
Infrastructures (2024) Vol. 9, Iss. 6, pp. 92-92
Open Access
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