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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:

Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models
Hongxia Ma, Jiandong Liu, Jia Zhang, et al.
Advances in Civil Engineering (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 36

Showing 1-25 of 36 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: 21
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: 15
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
Reimagining resources policy: Synergizing mining waste utilization for sustainable construction practices
Haoxuan Yu, Izni Zahidi, Chow Ming Fai, et al.
Journal of Cleaner Production (2024) Vol. 464, pp. 142795-142795
Open Access | Times Cited: 12
Machine learning for predicting compressive strength of sustainable cement paste incorporating copper mine tailings as supplementary cementitious materials
Eka Oktavia Kurniati, Hang Zeng, Marat I. Latypov, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03373-e03373
Open Access | Times Cited: 9
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
Evaluation of textile effluent treatment plant sludge as supplementary cementitious material in concrete using experimental and machine learning approaches
Md Mottakin, Shuvo Dip Datta, Md. Mehrab Hossain, et al.
Journal of Building Engineering (2024) Vol. 96, pp. 110627-110627
Closed Access | Times Cited: 8
Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method
Jiandong Huang, Mengmeng Zhou, Hongwei Yuan, et al.
Materials (2022) Vol. 15, Iss. 10, pp. 3500-3500
Open Access | Times Cited: 28
Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches
Muhammad Fawad, Hisham Alabduljabbar, Furqan Farooq, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Optimizing machine learning techniques and SHapley Additive exPlanations (SHAP) analysis for the compressive property of self-compacting concrete
Zhiyuan Wang, Huihui Liu, Muhammad Nasir Amin, et al.
Materials Today Communications (2024) Vol. 39, pp. 108804-108804
Closed Access | Times Cited: 5
Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design
Fei Zhu, Xiangping Wu, Mengmeng Zhou, et al.
Materials (2022) Vol. 15, Iss. 11, pp. 3833-3833
Open Access | Times Cited: 24
A Novel Neural Computing Model Applied to Estimate the Dynamic Modulus (DM) of Asphalt Mixtures by the Improved Beetle Antennae Search
Jiandong Huang, Mengmeng Zhou, Mohanad Muayad Sabri Sabri, et al.
Sustainability (2022) Vol. 14, Iss. 10, pp. 5938-5938
Open Access | Times Cited: 22
Machine learning models for predicting the compressive strength of agro-waste stabilized bricks for sustainable buildings
Ifeyinwa Ijeoma Obianyo, Jonathan Timothy Auta, David Sciacca, et al.
Deleted Journal (2024) Vol. 1, Iss. 1
Open Access | Times Cited: 4
Comparative use of different AI methods for the prediction of concrete compressive strength
Mouhamadou Amar
Cleaner Materials (2025) Vol. 15, pp. 100299-100299
Open Access
A comparative assessment of tree-based predictive models to estimate geopolymer concrete compressive strength
May Huu Nguyen, Hai‐Van Thi, Son Hoang Trinh, et al.
Neural Computing and Applications (2022) Vol. 35, Iss. 9, pp. 6569-6588
Closed Access | Times Cited: 21
Towards Sustainable Construction Materials: A Comparative Study of Prediction Models for Green Concrete with Metakaolin
Jiandong Huang, Mengmeng Zhou, Hongwei Yuan, et al.
Buildings (2022) Vol. 12, Iss. 6, pp. 772-772
Open Access | Times Cited: 20
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
Forecasting material quantity using machine learning and times series techniques
Hanane Zermane, Hassina MADJOUR, Ahcene Ziar, et al.
Journal of Electrical Engineering (2024) Vol. 75, Iss. 3, pp. 237-248
Open Access | Times Cited: 3
Prediction of compressive strength fiber-reinforced geopolymer concrete (FRGC) using gene expression programming (GEP)
M. A. Hossain, Md Nasir Uddin, Md Minaz Hossain
Materials Today Proceedings (2023)
Closed Access | Times Cited: 10
A boundary identification approach for the feasible space of structural optimization using a virtual sampling technique-based support vector machine
Hongyou Cao, Huiyang Li, Wen Sun, et al.
Computers & Structures (2023) Vol. 287, pp. 107118-107118
Closed Access | Times Cited: 9
Runtime-based metaheuristic prediction of the compressive strength of net-zero traditional concrete mixed with BFS, FA, SP considering multiple curing regimes
Kennedy C. Onyelowe, Denise‐Penelope N. Kontoni, Sita Rama Murty Pilla, et al.
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 2, pp. 1241-1253
Closed Access | Times Cited: 8
Predicting the compressive strength of foam concrete: an in-depth investigation employing material analysis and beetle antennae search-random forest modelling
Y. Sivananda Reddy, S. Anandh, S. Sindhu Nachiar
Innovative Infrastructure Solutions (2024) Vol. 9, Iss. 8
Closed Access | Times Cited: 2
Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber
Assia Aboubakar Mahamat, Moussa Mahamat Boukar, Nordine Leklou, et al.
Applied Sciences (2024) Vol. 14, Iss. 17, pp. 7540-7540
Open Access | Times Cited: 2
Hybrid extreme gradient boosting regressor models for the multi-objective mixture design optimization of cementitious mixtures incorporating mine tailings as fine aggregates
Chathuranga Balasooriya Arachchilage, Guangping Huang, Jian Zhao, et al.
Cement and Concrete Composites (2024), pp. 105787-105787
Open Access | Times Cited: 2
Mixture Optimization of Cementitious Materials Using Machine Learning and Metaheuristic Algorithms: State of the Art and Future Prospects
Yaxin Song, Xudong Wang, Houchang Li, et al.
Materials (2022) Vol. 15, Iss. 21, pp. 7830-7830
Open Access | Times Cited: 11
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