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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:

Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups
Junfei Zhang, Yuantian Sun, Guichen Li, et al.
Engineering With Computers (2020) Vol. 38, Iss. 2, pp. 1293-1307
Closed Access | Times Cited: 92

Showing 1-25 of 92 citing articles:

Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications
Shanaka Kristombu Baduge, P.S.M. Thilakarathna, Jude Shalitha Perera, et al.
Automation in Construction (2022) Vol. 141, pp. 104440-104440
Open Access | Times Cited: 530
Machine learning for structural engineering: A state-of-the-art review
Huu‐Tai Thai
Structures (2022) Vol. 38, pp. 448-491
Closed Access | Times Cited: 377
AI-based performance prediction for 3D-printed concrete considering anisotropy and steam curing condition
Xiaofei Yao, Xin Lyu, Junbo Sun, et al.
Construction and Building Materials (2023) Vol. 375, pp. 130898-130898
Closed Access | Times Cited: 90
Exploring temperature-resilient recycled aggregate concrete with waste rubber: An experimental and multi-objective optimization analysis
Yunchao Tang, Yufei Wang, Dongxiao Wu, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2023) Vol. 62, Iss. 1
Open Access | Times Cited: 87
Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review
Shiqi Wang, Peng Xia, Keyu Chen, et al.
Journal of Building Engineering (2023) Vol. 80, pp. 108065-108065
Closed Access | Times Cited: 57
Advancements in 3D printing of cementitious materials: A review of mineral additives, properties, and systematic developments
Osama Zaid, Mohamed Hechmi El Ouni
Construction and Building Materials (2024) Vol. 427, pp. 136254-136254
Closed Access | Times Cited: 22
Exploring LightGBM-SHAP: Interpretable predictive modeling for concrete strength under high temperature conditions
Shaoqiang Meng, Zhenming Shi, Chengzhi Xia, et al.
Structures (2025) Vol. 71, pp. 108134-108134
Closed Access | Times Cited: 2
Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods
Min Wang, Mingfeng Du, Timon Rabczuk, et al.
Frontiers of Structural and Civil Engineering (2025)
Closed Access | Times Cited: 1
A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams
Bo Fu, De‐Cheng Feng
Journal of Building Engineering (2020) Vol. 36, pp. 102118-102118
Closed Access | Times Cited: 95
Development of ensemble learning models to evaluate the strength of coal-grout materials
Yuantian Sun, Guichen Li, Nong Zhang, et al.
International Journal of Mining Science and Technology (2020) Vol. 31, Iss. 2, pp. 153-162
Open Access | Times Cited: 88
Mixture optimization for environmental, economical and mechanical objectives in silica fume concrete: A novel frame-work based on machine learning and a new meta-heuristic algorithm
Junfei Zhang, Yimiao Huang, Guowei Ma, et al.
Resources Conservation and Recycling (2021) Vol. 167, pp. 105395-105395
Open Access | Times Cited: 85
A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm
Jiandong Huang, Panagiotis G. Asteris, Siavash Manafi Khajeh Pasha, et al.
Engineering With Computers (2020) Vol. 38, Iss. 3, pp. 2209-2220
Closed Access | Times Cited: 77
Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential
Mingxiang Cai, Hocine Ouaer, Ahmed Salih Mohammed, et al.
Engineering With Computers (2021) Vol. 38, Iss. 4, pp. 3611-3623
Closed Access | Times Cited: 62
Shear strength prediction of reinforced concrete beams using machine learning
M.S. Sandeep, Koravith Tiprak, Sakdirat Kaewunruen, et al.
Structures (2022) Vol. 47, pp. 1196-1211
Open Access | Times Cited: 58
Machine learning (ML) based models for predicting the ultimate strength of rectangular concrete-filled steel tube (CFST) columns under eccentric loading
Chen Wang, Tak‐Ming Chan
Engineering Structures (2022) Vol. 276, pp. 115392-115392
Closed Access | Times Cited: 47
Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network
Junbo Sun, Jiaqing Wang, Zhaoyue Zhu, et al.
Buildings (2022) Vol. 12, Iss. 1, pp. 65-65
Open Access | Times Cited: 45
A multi-objective optimisation approach for activity excitation of waste glass mortar
Junbo Sun, Yunchao Tang, Jianqun Wang, et al.
Journal of Materials Research and Technology (2022) Vol. 17, pp. 2280-2304
Open Access | Times Cited: 42
Evolutionary optimization of machine learning algorithm hyperparameters for strength prediction of high-performance concrete
Sourav Singh, Sanjaya Kumar Patro, Suraj Kumar Parhi
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 8, pp. 3121-3143
Closed Access | Times Cited: 34
Prediction of shear behavior of glass FRP bars-reinforced ultra-highperformance concrete I-shaped beams using machine learning
Asif Ahmed, Md Nasir Uddin, Muhammad Akbar, et al.
International Journal of Mechanics and Materials in Design (2023) Vol. 20, Iss. 2, pp. 269-290
Open Access | Times Cited: 22
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: 10
Research on concrete early shrinkage characteristics based on machine learning algorithms for multi-objective optimization
Jianqun Wang, Heng Liu, Junbo Sun, et al.
Journal of Building Engineering (2024) Vol. 89, pp. 109415-109415
Closed Access | Times Cited: 9
Modeling carbonation depth of recycled aggregate concrete containing chlorinated salts
Feng Qu, Wen Xia, Congtao Sun, et al.
Construction and Building Materials (2024) Vol. 430, pp. 136478-136478
Closed Access | Times Cited: 9
A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering
Sparsh Sharma, Suhaib Ahmed, Mohd Naseem, et al.
Sensors (2021) Vol. 21, Iss. 2, pp. 463-463
Open Access | Times Cited: 54
Rockburst intensity evaluation by a novel systematic and evolved approach: machine learning booster and application
Yuantian Sun, Guichen Li, Junfei Zhang, et al.
Bulletin of Engineering Geology and the Environment (2021) Vol. 80, Iss. 11, pp. 8385-8395
Closed Access | Times Cited: 44
Machine-learning-based models to predict shear transfer strength of concrete joints
Tongxu Liu, Zhen Wang, Junlin Zeng, et al.
Engineering Structures (2021) Vol. 249, pp. 113253-113253
Closed Access | Times Cited: 42
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