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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 Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches
Zhu Yongzhong, Ayaz Ahmad, Waqas Ahmad, et al.
Crystals (2022) Vol. 12, Iss. 5, pp. 569-569
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning
Zhongjie Shen, Ahmed Farouk Deifalla, Paweł Kamiński, et al.
Materials (2022) Vol. 15, Iss. 10, pp. 3523-3523
Open Access | Times Cited: 78
Prediction of mechanical properties of eco-friendly concrete using machine learning algorithms and partial dependence plot analysis
Tonmoy Roy, Pobithra Das, Ravi Jagirdar, et al.
Smart Construction and Sustainable Cities (2025) Vol. 3, Iss. 1
Open Access | Times Cited: 3
Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques
Yongjian Li, Qizhi Zhang, Paweł Kamiński, et al.
Materials (2022) Vol. 15, Iss. 12, pp. 4209-4209
Open Access | Times Cited: 62
Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete
Kaffayatullah Khan, Waqas Ahmad, Muhammad Nasir Amin, et al.
Materials (2022) Vol. 15, Iss. 10, pp. 3430-3430
Open Access | Times Cited: 58
Artificial intelligence-based estimation of ultra-high-strength concrete's flexural property
Qichen Wang, Abasal Hussain, Muhammad Usman Farooqi, et al.
Case Studies in Construction Materials (2022) Vol. 17, pp. e01243-e01243
Open Access | Times Cited: 56
Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence
Zheng Dong, Rongxing Wu, Muhammad Sufian, et al.
Materials (2022) Vol. 15, Iss. 15, pp. 5194-5194
Open Access | Times Cited: 53
Assessing the compressive strength of self-compacting concrete with recycled aggregates from mix ratio using machine learning approach
P. Jagadesh, Jesús de Prado-Gil, Neemias Silva-Monteiro, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 1483-1498
Open Access | Times Cited: 39
Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods
Kaffayatullah Khan, Waqas Ahmad, Muhammad Nasir Amin, et al.
Heliyon (2023) Vol. 9, Iss. 5, pp. e16288-e16288
Open Access | Times Cited: 31
Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar
Muhammad Nasir Amin, Hassan Ali Alkadhim, Waqas Ahmad, et al.
PLoS ONE (2023) Vol. 18, Iss. 1, pp. e0280761-e0280761
Open Access | Times Cited: 28
Data-driven shear strength predictions of recycled aggregate concrete beams with /without shear reinforcement by applying machine learning approaches
Thushara Jayasinghe, Bo wei Chen, Zhaorui Zhang, et al.
Construction and Building Materials (2023) Vol. 387, pp. 131604-131604
Open Access | Times Cited: 24
Prediction of properties of recycled aggregate concrete using machine learning models: A critical review
Zengfeng Zhao, Yajie Liu, Yanyun Lu, et al.
Journal of Building Engineering (2024) Vol. 90, pp. 109516-109516
Closed Access | Times Cited: 15
Machine and Deep Learning Methods for Concrete Strength Prediction: A Bibliometric and Content Analysis Review of Research Trends and Future Directions
Raman Kumar, Essam Althaqafi, S. Gopal Krishna Patro, et al.
Applied Soft Computing (2024) Vol. 164, pp. 111956-111956
Closed Access | Times Cited: 14
Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete
Mohammed Alarfaj, Hisham Jahangir Qureshi, Muhammad Zubair Shahab, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02836-e02836
Open Access | Times Cited: 10
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
Tensile Strength Predictive Modeling of Natural-Fiber-Reinforced Recycled Aggregate Concrete Using Explainable Gradient Boosting Models
Celal Çakıroğlu, Farnaz Ahadian, Gebrai̇l Bekdaş, et al.
Journal of Composites Science (2025) Vol. 9, Iss. 3, pp. 119-119
Open Access | Times Cited: 1
Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar
Zhengyu Fei, Shixue Liang, Yiqing Cai, et al.
Materials (2023) Vol. 16, Iss. 2, pp. 583-583
Open Access | Times Cited: 18
A Systematic Review of the Research Development on the Application of Machine Learning for Concrete
Kaffayatullah Khan, Waqas Ahmad, Muhammad Nasir Amin, et al.
Materials (2022) Vol. 15, Iss. 13, pp. 4512-4512
Open Access | Times Cited: 26
Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete
Yunfeng Qian, Muhammad Sufian, Ahmad Hakamy, et al.
Frontiers in Materials (2023) Vol. 9
Open Access | Times Cited: 15
Machine learning models for estimating the compressive strength of rubberized concrete subjected to elevated temperature: Optimization and hyper-tuning
Turki S. Alahmari, Irfan Ullah, Furqan Farooq
Sustainable Chemistry and Pharmacy (2024) Vol. 42, pp. 101763-101763
Closed Access | Times Cited: 5
Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete
Dai Li, Wu Xu, Meirong Zhou, et al.
Materials (2022) Vol. 15, Iss. 13, pp. 4450-4450
Open Access | Times Cited: 21
A New Approach to Machine Learning Model Development for Prediction of Concrete Fatigue Life under Uniaxial Compression
Jaeho Son, Sung-Chul Yang
Applied Sciences (2022) Vol. 12, Iss. 19, pp. 9766-9766
Open Access | Times Cited: 20
A soft-computing-based modeling approach for predicting acid resistance of waste-derived cementitious composites
Qingyu Cao, Xiongzhou Yuan, Muhammad Nasir Amin, et al.
Construction and Building Materials (2023) Vol. 407, pp. 133540-133540
Closed Access | Times Cited: 12
A Robust LightGBM Model for Concrete Tensile Strength Forecast to Aid in Resilience-based Structure Strategies
Chukwuemeka Daniel
Heliyon (2024) Vol. 10, Iss. 20, pp. e39679-e39679
Open Access | Times Cited: 4
Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients
Xingchen Pan, Yixuan Xiao, Salman Ali Suhail, et al.
Materials (2022) Vol. 15, Iss. 12, pp. 4194-4194
Open Access | Times Cited: 17
Influence of cashew leaf ash as partial replacement for cement on the properties of fresh and hardened concrete
M.A. Kareem, B.B. Akintonde, J.S. Adesoye, et al.
Cleaner Waste Systems (2022) Vol. 4, pp. 100063-100063
Open Access | Times Cited: 15
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