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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 the Cement-Fly Ash–Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method
Jiandong Huang, Mohanad Muayad Sabri Sabri, Dmitrii Vladimirovich Ulrikh, et al.
Materials (2022) Vol. 15, Iss. 12, pp. 4193-4193
Open Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

Compressive strength of concrete material using machine learning techniques
Satish Paudel, Anil Pudasaini, Rajesh Kumar Shrestha, et al.
Cleaner Engineering and Technology (2023) Vol. 15, pp. 100661-100661
Open Access | Times Cited: 51
Machine learning and interactive GUI for concrete compressive strength prediction
Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi, Abdelrahman Kamal Hamed
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 31
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: 21
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: 18
Stacked-based machine learning to predict the uniaxial compressive strength of concrete materials
Abdelrahman Kamal Hamed, Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi
Computers & Structures (2025) Vol. 308, pp. 107644-107644
Closed Access | Times Cited: 2
Decision tree models for the estimation of geo-polymer concrete compressive strength
Ji Zhou, Zhanlin Su, Shahab Hosseini, et al.
Mathematical Biosciences & Engineering (2023) Vol. 21, Iss. 1, pp. 1413-1444
Open Access | Times Cited: 25
Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models
Ranran Wang, Jun Zhang, Yijun Lü, et al.
Buildings (2024) Vol. 14, Iss. 3, pp. 615-615
Open Access | Times Cited: 13
Exploring the viability of AI-aided genetic algorithms in estimating the crack repair rate of self-healing concrete
Qiong Tian, Yijun Lü, Ji Zhou, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 7
Prediction model for the compressive strength of green concrete using cement kiln dust and fly ash
Emad S. Bakhoum, Arsani Amir, Fady Osama, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 21
Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder
Muhammad Sarmad Mahmood, Ayub Elahi, Osama Zaid, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02557-e02557
Open Access | Times Cited: 17
Understanding Penetration Attenuation of Permeable Concrete: A Hybrid Artificial Intelligence Technique Based on Particle Swarm Optimization
Fei Zhu, Xiangping Wu, Yijun Lü, et al.
Buildings (2024) Vol. 14, Iss. 4, pp. 1173-1173
Open Access | Times Cited: 6
Compressive strength of waste-derived cementitious composites using machine learning
Qiong Tian, Yijun Lü, Ji Zhou, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 6
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
Harnessing Synergy of Machine Learning and Nature-Inspired Optimization for Enhanced Compressive Strength Prediction in Concrete
Abba Bashir, Esar Ahmad, Shashivendra Dulawat, et al.
Hybrid Advances (2025), pp. 100404-100404
Open Access
Firefly Optimization Heuristics for Sustainable Estimation in Power System Harmonics
Naveed Ahmed Malik, Naveed Ishtiaq Chaudhary, Muhammad Asif Zahoor Raja
Sustainability (2023) Vol. 15, Iss. 6, pp. 4816-4816
Open Access | Times Cited: 14
Biochar-enhanced concrete mixes: Pioneering multi-objective optimization
Yifei Chen, Zhenjie Zou, Jin Xue-li, et al.
Journal of Building Engineering (2024) Vol. 88, pp. 109263-109263
Closed Access | Times Cited: 4
Stratified Metamodeling to Predict Concrete Compressive Strength Using an Optimized Dual-Layered Architectural Framework
Geraldo F. Neto, Bruno da S. Macêdo, Tales Humberto de Aquino Boratto, et al.
Mathematical and Computational Applications (2025) Vol. 30, Iss. 1, pp. 16-16
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
Machine learning predictions of high-strength RCA concrete utilizing chemically activated fly ash and nano-silica
Muhammad Adil Khan, Muhammad Nadeem Ashraf, Kennedy C. Onyelowe, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag
Xiangping Wu, Fei Zhu, Mengmeng Zhou, et al.
Materials (2022) Vol. 15, Iss. 13, pp. 4582-4582
Open Access | Times Cited: 19
Predicting the drift capacity of precast concrete columns using explainable machine learning approach
Zhen Wang, Tongxu Liu, Zilin Long, et al.
Engineering Structures (2023) Vol. 282, pp. 115771-115771
Closed Access | Times Cited: 11
The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network
Gaoming Yu, Senlai Zhu, Ziru Xiang
Buildings (2024) Vol. 14, Iss. 4, pp. 907-907
Open Access | Times Cited: 3
Predicting concrete strength early age using a combination of machine learning and electromechanical impedance with nano-enhanced sensors
Huang Ju, Lin Xing, Alaa H. Ali, et al.
Environmental Research (2024) Vol. 258, pp. 119248-119248
Closed Access | Times Cited: 3
The Time Variation Law of Concrete Compressive Strength: A Review
Weina Wang, Qingxia Yue
Applied Sciences (2023) Vol. 13, Iss. 8, pp. 4947-4947
Open Access | Times Cited: 8
Machine learning prediction of concrete frost resistance and optimization design of mix proportions
Jinpeng Dai, Zhijie Zhang, Xiaoyuan Yang, et al.
Journal of Intelligent & Fuzzy Systems (2024), pp. 1-26
Closed Access | Times Cited: 2
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