Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2016/09/01
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Indian UGC (journal)
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Refrences156
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Citations25
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Jian Zhou School of Resources and Safety Engineering, Central South Univ., #932 Lushan South Rd., Changsha 410083, China; Visiting Scholar, Dept. of Mining and Materials Engineering, McGill Univ., 3450 University St., Montreal, QC, Canada H3A 0E8 (corresponding author).
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Xibing Li Professor, School of Resources and Safety Engineering, Central South Univ., #932 Lushan South Rd., Changsha 410083, China.
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Hani S. Mitri Professor, Dept. of Mining and Materials Engineering, McGill Univ., 3450 University St., Montreal, QC, Canada H3A 0E8.
Cite
Zhou, Jian, et al. “Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods”. Journal of Computing in Civil Engineering, vol. 30, no. 5, 2016, https://doi.org/10.1061/(asce)cp.1943-5487.0000553.
Zhou, J., Li, X., & Mitri, H. S. (2016). Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods. Journal of Computing in Civil Engineering, 30(5). https://doi.org/10.1061/(asce)cp.1943-5487.0000553
Zhou, Jian, Xibing Li, and Hani S. Mitri. “Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods”. Journal of Computing in Civil Engineering 30, no. 5 (2016). https://doi.org/10.1061/(asce)cp.1943-5487.0000553.
Zhou J, Li X, Mitri HS. Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods. Journal of Computing in Civil Engineering. 2016;30(5).
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Technology
Engineering (General)
Civil engineering (General)
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Rockburst criterion based on artificial neural networks and nonlinear regression | 2013 | |||
| Development and validation of rockburst vulnerability index (RVI) in deep hard rock tunnels | 2011 | |||
| Dynamic problems in deep exploitation of hard rock metal mines | 2011 | |||
| Neural network and its application to predict rock burst | 2011 | |||
| Application of extension evaluation method in rockburst prediction based on rough set theory | 2010 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Rockburst Intensity Grade Prediction Based on Data Preprocessing Techniques and Multi-model Ensemble Learning Algorithms | Rock Mechanics and Rock Engineering |
| 2024 | |
| A new empirical chart for coal burst liability classification using Kriging method | Journal of Central South University |
| 6 | 2023 |
| Prophetical Modeling Using Limit Equilibrium Method and Novel Machine Learning Ensemble for Slope Stability Gauging in Kalimpong | Iranian Journal of Science and Technology, Transactions of Civil Engineering |
| 1 | 2023 |
| Combining machine learning and numerical modelling for rockburst prediction | Geomechanics and Geoengineering |
| 2 | 2023 |
How Do Vehicles Make Decisions during Implementation Period of Discretionary Lane Change? A Data-Driven Research | Journal of Advanced Transportation |
| 1 | 2023 |
Citations Analysis
The category
Technology: Engineering (General). Civil engineering (General) 14
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Dynamic Compressive Characteristics of Sandstone under Confining Pressure and Radial Gradient Stress with the SHPB Test and was published in 2018. The most recent citation comes from a 2024 study titled Rockburst Intensity Grade Prediction Based on Data Preprocessing Techniques and Multi-model Ensemble Learning Algorithms. This article reached its peak citation in 2022, with 6 citations. It has been cited in 15 different journals, 46% of which are open access. Among related journals, the Advances in Civil Engineering cited this research the most, with 6 citations. The chart below illustrates the annual citation trends for this article.