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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:

Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods
Jian Zhou, Xibing Li, Hani S. Mitri
Journal of Computing in Civil Engineering (2016) Vol. 30, Iss. 5
Closed Access | Times Cited: 383

Showing 1-25 of 383 citing articles:

Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization
Wengang Zhang, Chongzhi Wu, Haiyi Zhong, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 1, pp. 469-477
Open Access | Times Cited: 684
State-of-the-art review of soft computing applications in underground excavations
Wengang Zhang, Runhong Zhang, Chongzhi Wu, et al.
Geoscience Frontiers (2019) Vol. 11, Iss. 4, pp. 1095-1106
Open Access | Times Cited: 408
Evaluation method of rockburst: State-of-the-art literature review
Jian Zhou, Xibing Li, Hani S. Mitri
Tunnelling and Underground Space Technology (2018) Vol. 81, pp. 632-659
Closed Access | Times Cited: 371
Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration
Yingui Qiu, Jian Zhou, Manoj Khandelwal, et al.
Engineering With Computers (2021) Vol. 38, Iss. S5, pp. 4145-4162
Closed Access | Times Cited: 320
A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model
Jin Duan, Panagiotis G. Asteris, Hoang Nguyen, et al.
Engineering With Computers (2020) Vol. 37, Iss. 4, pp. 3329-3346
Closed Access | Times Cited: 281
Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories
Jian Zhou, Enming Li, Shan Yang, et al.
Safety Science (2019) Vol. 118, pp. 505-518
Closed Access | Times Cited: 276
Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate
Jian Zhou, Yingui Qiu, Shuangli Zhu, et al.
Engineering Applications of Artificial Intelligence (2020) Vol. 97, pp. 104015-104015
Closed Access | Times Cited: 262
Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques
Jian Zhou, Yingui Qiu, Danial Jahed Armaghani, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101091-101091
Open Access | Times Cited: 231
Feasibility of Random-Forest Approach for Prediction of Ground Settlements Induced by the Construction of a Shield-Driven Tunnel
Jian Zhou, Xiuzhi Shi, Kun Du, et al.
International Journal of Geomechanics (2016) Vol. 17, Iss. 6
Closed Access | Times Cited: 211
Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate
Hai Xu, Jian Zhou, Panagiotis G. Asteris, et al.
Applied Sciences (2019) Vol. 9, Iss. 18, pp. 3715-3715
Open Access | Times Cited: 209
Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization
Jian Zhou, Yingui Qiu, Shuangli Zhu, et al.
Underground Space (2020) Vol. 6, Iss. 5, pp. 506-515
Open Access | Times Cited: 209
Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials
Jian Zhou, Enming Li, Haixia Wei, et al.
Applied Sciences (2019) Vol. 9, Iss. 8, pp. 1621-1621
Open Access | Times Cited: 206
Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns
Payam Sarir, Jun Chen, Panagiotis G. Asteris, et al.
Engineering With Computers (2019) Vol. 37, Iss. 1, pp. 1-19
Closed Access | Times Cited: 204
Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance
So-Young Park, Jinsoo Kim
Applied Sciences (2019) Vol. 9, Iss. 5, pp. 942-942
Open Access | Times Cited: 203
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
Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models
Jian Zhou, Panagiotis G. Asteris, Danial Jahed Armaghani, et al.
Soil Dynamics and Earthquake Engineering (2020) Vol. 139, pp. 106390-106390
Closed Access | Times Cited: 178
Rockburst characteristics and numerical simulation based on a strain energy density index: A case study of a roadway in Linglong gold mine, China
Lei Weng, Linqi Huang, Abbas Taheri, et al.
Tunnelling and Underground Space Technology (2017) Vol. 69, pp. 223-232
Closed Access | Times Cited: 172
Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning
Shaokang Hou, Yaoru Liu, Qiang Yang
Journal of Rock Mechanics and Geotechnical Engineering (2021) Vol. 14, Iss. 1, pp. 123-143
Open Access | Times Cited: 169
Assessment of pile drivability using random forest regression and multivariate adaptive regression splines
Wengang Zhang, Chongzhi Wu, Yongqin Li, et al.
Georisk Assessment and Management of Risk for Engineered Systems and Geohazards (2019) Vol. 15, Iss. 1, pp. 27-40
Closed Access | Times Cited: 167
Prediction of shield tunneling-induced ground settlement using machine learning techniques
Renpeng Chen, Pin Zhang, Huai-Na Wu, et al.
Frontiers of Structural and Civil Engineering (2019) Vol. 13, Iss. 6, pp. 1363-1378
Closed Access | Times Cited: 157
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: 155
A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest
Pin Zhang, Zhen‐Yu Yin, Yin-Fu Jin, et al.
Engineering Geology (2019) Vol. 265, pp. 105328-105328
Open Access | Times Cited: 155
Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost
Xiliang Zhang, Hoang Nguyen, Xuan-Nam Bui, et al.
Natural Resources Research (2019) Vol. 29, Iss. 2, pp. 711-721
Closed Access | Times Cited: 154
Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations
Jian Zhou, Yingui Qiu, Manoj Khandelwal, et al.
International Journal of Rock Mechanics and Mining Sciences (2021) Vol. 145, pp. 104856-104856
Open Access | Times Cited: 152
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
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