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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.

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Showing 19 citing articles:

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
Building the future: Smart concrete as a key element in next-generation construction
Qiong Tian, Ji Zhou, Jingwei Hou, et al.
Construction and Building Materials (2024) Vol. 429, pp. 136364-136364
Closed Access | Times Cited: 18
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
Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor
Qiang Wang, Tao Cheng, Yijun Lü, et al.
Sensors (2024) Vol. 24, Iss. 4, pp. 1285-1285
Open Access | Times Cited: 16
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
Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites (ECC)
Waleed Bin Inqiad, Muhammad Faisal Javed, Muhammad Shahid Siddique, et al.
Materials Today Communications (2024) Vol. 39, pp. 109222-109222
Closed Access | Times Cited: 10
Ensemble machine learning algorithms for efficient prediction of compressive strength of concrete containing tyre rubber and brick powder
David Sinkhonde, Tajebe Bezabih, Derrick Mirindi, et al.
Cleaner Waste Systems (2025), pp. 100236-100236
Open Access | Times Cited: 1
Performance optimisation and predictive modelling of rice husk ash recycled concrete under the coupled action of freeze-thaw cycles and chloride erosion: Experimental study and machine learning
Wei Zhang, Zhenhua Duan, Chao Liu, et al.
Construction and Building Materials (2025) Vol. 481, pp. 141467-141467
Closed Access | Times Cited: 1
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: 8
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: 8
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
Utilizing Construction and Demolition Waste in Concrete as a Sustainable Cement Substitute: A Comprehensive Study on Behavior Under Short-term Dynamic and Static Loads via Laboratory and Numerical Analysis
Mohammad Mohtasham Moein, Komeil Rahmati, Ali Mohtasham Moein, et al.
Journal of Building Engineering (2024), pp. 110778-110778
Closed Access | Times Cited: 6
Performance Comparison of Artificial Neural Network and Random Forest Models for Predicting the Compressive Strength of Fibre-Reinforced GGBS-Based Geopolymer Concrete Composites
Shimol Philip, M. Nidhi
Materials Circular Economy (2024) Vol. 6, Iss. 1
Closed Access | Times Cited: 4
Compressive and tensile strength estimation of sustainable geopolymer concrete using contemporary boosting ensemble techniques
Ji Zhou, Qiong Tian, Ayaz Ahmad, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 3
Performance evaluation of hybrid fiber-reinforced concrete based on electrical resistivity: Experimental and data-driven method
Zhen Sun, Yalin Li, Tianran Han, et al.
Construction and Building Materials (2024) Vol. 446, pp. 137992-137992
Closed Access | Times Cited: 3
Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning
Shan Wang, Hongtao Wang, Yijun Lü, et al.
Applied Sciences (2024) Vol. 14, Iss. 12, pp. 5020-5020
Open Access | Times Cited: 2
Comparison of Predictive Modeling Concrete Compressive Strength with Machine Learning Approaches
Gregorius Airlangga
UKaRsT (2024) Vol. 8, Iss. 1, pp. 28-41
Open Access | Times Cited: 2
Comprehensive review of 3D printed concrete, life cycle assessment, AI and ML models: Materials, engineered properties and techniques for additive manufacturing
Mueen Alam Khan, Aayzaz Ahmed, Tariq Ali, et al.
Sustainable materials and technologies (2024) Vol. 43, pp. e01164-e01164
Closed Access | Times Cited: 1
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