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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:

Forecasting unconfined compressive strength of calcium sulfoaluminate cement mixtures using ensemble machine learning techniques integrated with shapely-additive explanations
Chathuranga Balasooriya Arachchilage, Guangping Huang, Chengkai Fan, et al.
Construction and Building Materials (2023) Vol. 409, pp. 134083-134083
Closed Access | Times Cited: 13

Showing 13 citing articles:

Assessing the significance of the particle size of Ganga sand Sone sand and bentonite mixtures for hydraulic containment liners integrated with machine learning-based UCS predictions
Rajiv Kumar, Divesh Ranjan Kumar, Sunita Kumari, et al.
Construction and Building Materials (2025) Vol. 465, pp. 140236-140236
Closed Access | Times Cited: 1
Geotechnical Characterization and Stability Prediction of Nano-Silica-Stabilized Slopes: A Machine Learning Approach to Mitigating Geological Hazards
Ishwor Thapa, Sufyan Ghani, Sunita Kumari, et al.
Transportation Infrastructure Geotechnology (2025) Vol. 12, Iss. 2
Open Access
Rapid Estimation of Truck Cycle Time in Open-Pit Mine Haulage Based on Feature-Optimized Machine Learning
Chengkai Fan, Na Zhang, Bei Jiang, et al.
Mining Metallurgy & Exploration (2025)
Closed Access
Deep Neural Network Models for Improving Truck Productivity Prediction in Open-pit Mines
Ömer Faruk Uğurlu, Chengkai Fan, Bei Jiang, et al.
Mining Metallurgy & Exploration (2024) Vol. 41, Iss. 2, pp. 619-636
Closed 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
Estimation of compressive strength of concrete with manufactured sand and natural sand using interpretable artificial intelligence
Xiaodong Liu, Shengqi Mei, Xingju Wang, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03840-e03840
Open Access | Times Cited: 2
Predicting bond strength of corroded reinforced concrete after high-temperature exposure: A stacking model and feature selection
Peng Ge, Ou Yang, Xugang Hua, et al.
Construction and Building Materials (2024) Vol. 456, pp. 139290-139290
Closed Access | Times Cited: 2
A Novel Historical Data-Based Method for Predicting Firefighters Demand in Urban Fires
Chen-yue Zhang, Rui Zhao, Ning Wang, et al.
Fire Safety Journal (2024) Vol. 147, pp. 104200-104200
Closed Access | Times Cited: 1
In-depth insight into the driving factors of the compressive strength development of MKPC based on interpretable machine learning methods
Shanliang Ma, Jiarui Gu, Jie Wang, et al.
Journal of Industrial and Engineering Chemistry (2024)
Closed Access | Times Cited: 1
Machine learning-assisted characterization of the thermal conductivity of cement-based grouts for borehole heat exchangers
Jian Zhao, Chengkai Fan, Guangping Huang, et al.
Construction and Building Materials (2024) Vol. 449, pp. 138506-138506
Open Access | Times Cited: 1
Interpretable machine‐learning models for predicting creep recovery of concrete
Shengqi Mei, Xiaodong Liu, Xingju Wang, et al.
Structural Concrete (2024)
Closed Access
Novel Fast Predesign Methodology of Centrifugal Compressor Used for Pemfcs Based on 1d Loss Mechanism and Interpretable Machine Learning Models
Zhikai Chen, Jieshuai Sun, Ziying Chen, et al.
(2024)
Closed Access
Fast predesign methodology of centrifugal compressor for PEMFCs combining a physics-based loss model and an interpretable machine learning method
Zhikai Chen, Jieshuai Sun, Ziying Chen, et al.
International Journal of Hydrogen Energy (2024) Vol. 90, pp. 353-368
Closed Access
Elucidating Rheological Properties of Cementitious Materials Containing Fly Ash and Nanosilica by Machine Learning
Amy Tian, Yue Gu, Zhenhua Wei, et al.
Nanomaterials (2024) Vol. 14, Iss. 21, pp. 1700-1700
Open Access
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