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:

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines
Chengkai Fan, Na Zhang, Bei Jiang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2023) Vol. 16, Iss. 3, pp. 727-740
Open Access | Times Cited: 11

Showing 11 citing articles:

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

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

Machine learning models for predicting rock fracture toughness at different temperature conditions
Xunjian Hu, Dong Liao, Dongdong Ma, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02622-e02622
Open Access | Times Cited: 8

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

Study on multi-factor casing damage prediction method based on machine learning
Fuli Li, Wei Yan, Xianyong Kong, et al.
Energy (2024) Vol. 296, pp. 131044-131044
Closed Access | Times Cited: 2

Machine learning with SHapley additive exPlanations for evaluating mine truck productivity under real-site weather conditions at varying temporal resolutions
Chengkai Fan, Chathuranga Balasooriya Arachchilage, Na Zhang, et al.
International Journal of Mining Reclamation and Environment (2024) Vol. 38, Iss. 10, pp. 810-832
Closed Access | Times Cited: 2

Influence of water-to-binder ratios on the performance of limestone calcined clay cement-based paste for mining applications
Zhiqiang Feng, Jian Zhao, Guangping Huang, et al.
Deleted Journal (2024) Vol. 1, Iss. 3, pp. 262-272
Open Access | Times Cited: 2

A novel identification technology and real-time classification forecasting model based on hybrid machine learning methods in mixed weathered mudstone-sand-pebble formation
Yong Zeng, Yingjie Wei, Yuyou Yang
Tunnelling and Underground Space Technology (2024) Vol. 153, pp. 106045-106045
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

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

Predicting Rock Fracture Toughness Using a KOA-BP Neural Network: A Case Study from Five Provinces in China
Zehang Wang, Lei Yu, Shuaishuai Niu, et al.
Geotechnical and Geological Engineering (2024) Vol. 42, Iss. 8, pp. 7963-7977
Closed Access

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