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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 methods for rockburst prediction-state-of-the-art review
Yuanyuan Pu, Derek B. Apel, Victor Liu, et al.
International Journal of Mining Science and Technology (2019) Vol. 29, Iss. 4, pp. 565-570
Open Access | Times Cited: 156

Showing 1-25 of 156 citing articles:

Experimental study on the mechanical and failure behaviors of deep rock subjected to true triaxial stress: A review
Heping Xie, Jun Lü, Cunbao Li, et al.
International Journal of Mining Science and Technology (2022) Vol. 32, Iss. 5, pp. 915-950
Open Access | Times Cited: 241
A review of rockburst: Experiments, theories, and simulations
Manchao He, Tai Cheng, Yafei Qiao, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 15, Iss. 5, pp. 1312-1353
Open Access | Times Cited: 179
Application of artificial intelligence to rock mechanics: An overview
Abiodun Ismail Lawal, Sangki Kwon
Journal of Rock Mechanics and Geotechnical Engineering (2020) Vol. 13, Iss. 1, pp. 248-266
Open Access | Times Cited: 141
Prediction of rockhead using a hybrid N-XGBoost machine learning framework
Xing Zhu, Jian Chu, Kangda Wang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2021) Vol. 13, Iss. 6, pp. 1231-1245
Open Access | Times Cited: 114
Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models
Xin Yin, Quansheng Liu, Yucong Pan, et al.
Natural Resources Research (2021) Vol. 30, Iss. 2, pp. 1795-1815
Closed Access | Times Cited: 107
Fusion of finite element and machine learning methods to predict rock shear strength parameters
Defu Zhu, Biaobiao Yu, Deyu Wang, et al.
Journal of Geophysics and Engineering (2024) Vol. 21, Iss. 4, pp. 1183-1193
Open Access | Times Cited: 93
Short-term rockburst prediction in underground project: insights from an explainable and interpretable ensemble learning model
Yingui Qiu, Jian Zhou
Acta Geotechnica (2023) Vol. 18, Iss. 12, pp. 6655-6685
Closed Access | Times Cited: 68
Short-Term Rockburst Damage Assessment in Burst-Prone Mines: An Explainable XGBOOST Hybrid Model with SCSO Algorithm
Yingui Qiu, Jian Zhou
Rock Mechanics and Rock Engineering (2023) Vol. 56, Iss. 12, pp. 8745-8770
Closed Access | Times Cited: 59
A strength-stress coupling criterion for rockburst: Inspirations from 1114 rockburst cases in 197 underground rock projects
Fengqiang Gong, Jinhao Dai, Lei Xu
Tunnelling and Underground Space Technology (2023) Vol. 142, pp. 105396-105396
Closed Access | Times Cited: 58
Rockburst prediction and prevention in underground space excavation
Jian Zhou, Yulin Zhang, Chuanqi Li, et al.
Underground Space (2023) Vol. 14, pp. 70-98
Open Access | Times Cited: 55
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
Numerical modeling for rockbursts: A state-of-the-art review
Jun Wang, Derek B. Apel, Yuanyuan Pu, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2020) Vol. 13, Iss. 2, pp. 457-478
Open Access | Times Cited: 71
Real-time prediction of rockburst intensity using an integrated CNN-Adam-BO algorithm based on microseismic data and its engineering application
Xin Yin, Quansheng Liu, Xing Huang, et al.
Tunnelling and Underground Space Technology (2021) Vol. 117, pp. 104133-104133
Closed Access | Times Cited: 71
Predictive Modeling of Short-Term Rockburst for the Stability of Subsurface Structures Using Machine Learning Approaches: t-SNE, K-Means Clustering and XGBoost
Barkat Ullah, Muhammad Kamran, Yichao Rui
Mathematics (2022) Vol. 10, Iss. 3, pp. 449-449
Open Access | Times Cited: 68
A new empirical chart for rockburst analysis in tunnelling: Tunnel rockburst classification (TRC)
Hadi Farhadian
International Journal of Mining Science and Technology (2021) Vol. 31, Iss. 4, pp. 603-610
Open Access | Times Cited: 65
Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest
Diyuan Li, Zida Liu, Danial Jahed Armaghani, et al.
Mathematics (2022) Vol. 10, Iss. 5, pp. 787-787
Open Access | Times Cited: 57
Novel ensemble intelligence methodologies for rockburst assessment in complex and variable environments
Diyuan Li, Zida Liu, Danial Jahed Armaghani, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 50
Comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation signals of rock burst based on deep learning
Yangyang Di, Enyuan Wang, Zhonghui Li, et al.
International Journal of Rock Mechanics and Mining Sciences (2023) Vol. 170, pp. 105519-105519
Closed Access | Times Cited: 42
Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms
Chuanqi Li, Jian Zhou, Kun Du, et al.
International Journal of Mining Science and Technology (2023) Vol. 33, Iss. 8, pp. 1019-1036
Open Access | Times Cited: 41
Application of KNN-based isometric mapping and fuzzy c-means algorithm to predict short-term rockburst risk in deep underground projects
Muhammad Kamran, Barkat Ullah, Mahmood Ahmad, et al.
Frontiers in Public Health (2022) Vol. 10
Open Access | Times Cited: 39
Rockburst prediction model using machine learning based on microseismic parameters of Qinling water conveyance tunnel
Ke Ma, Qingqing Shen, Xingye Sun, et al.
Journal of Central South University (2023) Vol. 30, Iss. 1, pp. 289-305
Closed Access | Times Cited: 34
A rockburst prediction model based on extreme learning machine with improved Harris Hawks optimization and its application
Mingliang Li, Kegang Li, Qingci Qin
Tunnelling and Underground Space Technology (2023) Vol. 134, pp. 104978-104978
Closed Access | Times Cited: 27
Microseismic event waveform classification using CNN-based transfer learning models
Longjun Dong, Hongmei Shu, Zheng Tang, et al.
International Journal of Mining Science and Technology (2023) Vol. 33, Iss. 10, pp. 1203-1216
Open Access | Times Cited: 25
Application of KM-SMOTE for rockburst intelligent prediction
Qiushi Liu, Yiguo Xue, Guangkun Li, et al.
Tunnelling and Underground Space Technology (2023) Vol. 138, pp. 105180-105180
Closed Access | Times Cited: 24
Combined prediction of rockburst based on multiple factors and stacking ensemble algorithm
H. Luo, Yong Fang, Jianfeng Wang, et al.
Underground Space (2023) Vol. 13, pp. 241-261
Open Access | Times Cited: 24
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