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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:

Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms
Mohammad Sadegh Barkhordari, Danial Jahed Armaghani, Ahmed Salih Mohammed, et al.
Buildings (2022) Vol. 12, Iss. 2, pp. 132-132
Open Access | Times Cited: 82

Showing 1-25 of 82 citing articles:

Machine Learning-Based Method for Predicting Compressive Strength of Concrete
Daihong Li, Zhili Tang, Qian Kang, et al.
Processes (2023) Vol. 11, Iss. 2, pp. 390-390
Open Access | Times Cited: 59
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: 39
Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm
Jun Zhang, Ranran Wang, Yijun Lü, et al.
Buildings (2024) Vol. 14, Iss. 3, pp. 591-591
Open Access | Times Cited: 26
Stabilization of iron ore tailing with low-carbon lime/carbide slag-activated ground granulated blast-furnace slag and coal fly ash
Xiqing Jiang, Lei Lang, Shiyu Liu, et al.
Construction and Building Materials (2024) Vol. 413, pp. 134946-134946
Closed Access | Times Cited: 20
Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete
Ranran Wang, Jun Zhang, Yijun Lü, et al.
Buildings (2024) Vol. 14, Iss. 2, pp. 396-396
Open Access | Times Cited: 19
Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete
Xuyang Shi, Shuzhao Chen, Qiang Wang, et al.
Gels (2024) Vol. 10, Iss. 2, pp. 148-148
Open Access | Times Cited: 17
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 | Times Cited: 3
Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms
Hong‐Gen Chen, Xin Li, Yanqi Wu, et al.
Buildings (2022) Vol. 12, Iss. 3, pp. 302-302
Open Access | Times Cited: 65
Super learner machine‐learning algorithms for compressive strength prediction of high performance concrete
Seunghye Lee, Ngoc‐Hien Nguyen, Armağan Karamanlı, et al.
Structural Concrete (2022) Vol. 24, Iss. 2, pp. 2208-2228
Closed Access | Times Cited: 52
A Review on the Effect of Mechanical Properties and Durability of Concrete with Construction and Demolition Waste (CDW) and Fly Ash in the Production of New Cement Concrete
Sérgio Roberto da Silva, Jairo José de Oliveira Andrade
Sustainability (2022) Vol. 14, Iss. 11, pp. 6740-6740
Open Access | Times Cited: 44
Effects of GBFS content and curing methods on the working performance and microstructure of ternary geopolymers based on high-content steel slag
Xinkui Yang, Shaopeng Wu, Shi Xu, et al.
Construction and Building Materials (2023) Vol. 410, pp. 134128-134128
Open Access | Times Cited: 41
Review on zero waste strategy for urban construction and demolition waste: Full component resource utilization approach for sustainable and low-carbon
Qiang Gao, Xi-guang Li, Si-qi Jiang, et al.
Construction and Building Materials (2023) Vol. 395, pp. 132354-132354
Closed Access | Times Cited: 33
Ensemble XGBoost schemes for improved compressive strength prediction of UHPC
May Huu Nguyen, Thuy‐Anh Nguyen, Haï-Bang Ly
Structures (2023) Vol. 57, pp. 105062-105062
Closed Access | Times Cited: 31
Unboxing machine learning models for concrete strength prediction using XAI
Sara Elhishi, Asmaa Mohammed Elashry, Sara El‐Metwally
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 26
Efficient compressive strength prediction of concrete incorporating recycled coarse aggregate using Newton’s boosted backpropagation neural network (NB-BPNN)
Rupesh Kumar Tipu, Vandna Batra, Suman Suman, et al.
Structures (2023) Vol. 58, pp. 105559-105559
Closed Access | Times Cited: 25
Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests
Yuzhen Wang, Mahdi Hasanipanah, Ahmad Safuan A. Rashid, et al.
Materials (2023) Vol. 16, Iss. 10, pp. 3731-3731
Open Access | Times Cited: 23
Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature
Abdulaziz Alaskar, Ghasan Alfalah, Fadi Althoey, et al.
Case Studies in Construction Materials (2023) Vol. 18, pp. e02199-e02199
Open Access | Times Cited: 23
An innovative approach to fly ash-based geopolymer concrete mix design: Utilizing 100 % recycled aggregates
Banoth Gopalakrishna, P. Dinakar
Structures (2024) Vol. 66, pp. 106819-106819
Closed Access | Times Cited: 15
Elucidating the reaction of seashell powder within fly ash cement: A focus on hydration products
Xiaowei Gu, Bohan Yang, Zhijun Li, et al.
Construction and Building Materials (2024) Vol. 428, pp. 136331-136331
Closed Access | Times Cited: 12
Quantifying compressive strength in limestone powder incorporated concrete with incorporating various machine learning algorithms with SHAP analysis
Mihir Mishra
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 11
Exploring the potential of soft computing for predicting compressive strength and slump flow diameter in fly ash-modified self-compacting concrete
Brwa Omer, Dilshad Kakasor Ismael Jaf, Sirwan Khuthur Malla, et al.
Archives of Civil and Mechanical Engineering (2024) Vol. 24, Iss. 2
Closed Access | Times Cited: 10
Sustainable innovation in self-compacted concrete: Integrating by-products and waste rubber for green construction practices
Yarivan J. Zrar, Payam Ismael Abdulrahman, Aryan Far H. Sherwani, et al.
Structures (2024) Vol. 62, pp. 106234-106234
Closed Access | Times Cited: 10
Evaluating the effect of high fly ash content and low curing temperature on early hydration heat of blended cement based on isothermal calorimetric method
Lin Li, Tengteng Feng, Yizheng Li, et al.
Construction and Building Materials (2024) Vol. 430, pp. 136110-136110
Closed Access | Times Cited: 10
Machine Learning Prediction Model Integrating Experimental Study for Compressive Strength of Carbon-Nanotubes Composites
Aneel Manan, Pu Zhang, Shoaib Ahmad, et al.
Journal of Engineering Research (2024)
Open Access | Times Cited: 9
Assessment of compressive strength of eco-concrete reinforced using machine learning tools
Houcine Bentegri, Mohamed Rabehi, Samir Kherfane, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
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