| Machine learning methods for rockburst prediction-state-of-the-art review Y Pu, DB Apel, V Liu, H Mitri International Journal of Mining Science and Technology 29 (4), 565-570, 2019 | 174 | 2019 |
| Image recognition of coal and coal gangue using a convolutional neural network and transfer learning Y Pu, DB Apel, A Szmigiel, J Chen Energies 12 (9), 1735, 2019 | 134 | 2019 |
| Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier Y Pu, DB Apel, H Xu Tunnelling and Underground Space Technology 90, 12-18, 2019 | 115 | 2019 |
| Numerical modeling for rockbursts: A state-of-the-art review J Wang, DB Apel, Y Pu, R Hall, C Wei, M Sepehri Journal of Rock Mechanics and Geotechnical Engineering 13 (2), 457-478, 2021 | 91 | 2021 |
| Rockburst prediction in kimberlite using decision tree with incomplete data Y Pu, DB Apel, B Lingga Journal of Sustainable Mining 17 (3), 158-165, 2018 | 91 | 2018 |
| Using machine learning approach for microseismic events recognition in underground excavations: Comparison of ten frequently-used models Y Pu, DB Apel, R Hall Engineering Geology 268, 105519, 2020 | 87 | 2020 |
| Risk assessment of dynamic disasters in deep coal mines based on multi-source, multi-parameter indexes, and engineering application J Du, J Chen, Y Pu, D Jiang, L Chen, Y Zhang Process Safety and Environmental Protection 155, 575-586, 2021 | 71 | 2021 |
| Experimental study on uniaxial compression failure modes and acoustic emission characteristics of fissured sandstone under water saturation J Chen, Y Ye, Y Pu, W Xu, D Mengli Theoretical and Applied Fracture Mechanics 119, 103359, 2022 | 50 | 2022 |
| Evaluation of burst liability in kimberlite using support vector machine Y Pu, DB Apel, C Wang, B Wilson Acta Geophysica 66, 973-982, 2018 | 46 | 2018 |
| Assessment of digital image correlation method in determining large scale cemented rockfill strains BA Lingga, DB Apel, M Sepehri, Y Pu International Journal of Mining Science and Technology 29 (5), 771-776, 2019 | 41 | 2019 |
| A principal component analysis/fuzzy comprehensive evaluation for rockburst potential in kimberlite Y Pu, D Apel, H Xu Pure and Applied Geophysics 175 (6), 2141-2151, 2018 | 37 | 2018 |
| FlotationNet: A hierarchical deep learning network for froth flotation recovery prediction Y Pu, A Szmigiel, J Chen, DB Apel Powder Technology 375, 317-326, 2020 | 36 | 2020 |
| A quantitative pre-warning for coal burst hazardous zones in a deep coal mine based on the spatio-temporal forecast of microseismic events J Chen, C Zhu, J Du, Y Pu, P Pan, J Bai, Q Qi Process Safety and Environmental Protection 159, 1105-1112, 2022 | 30 | 2022 |
| Purities prediction in a manufacturing froth flotation plant: The deep learning techniques Y Pu, A Szmigiel, DB Apel Neural Computing and Applications 32 (17), 13639-13649, 2020 | 29 | 2020 |
| Study on instability fracture and simulation of surrounding rock induced by fault activation under mining influence J Chen, K Shi, Y Pu, DB Apel, C Zhang, Y Zuo, J Chen, L Xu, Z Gui, ... Rock Mechanics Bulletin 2 (2), 100037, 2023 | 28 | 2023 |
| Applying machine learning approaches to evaluating rockburst liability: a comparation of generative and discriminative models Y Pu, DB Apel, C Wei Pure and Applied Geophysics 176 (10), 4503-4517, 2019 | 21 | 2019 |
| Step-path failure mechanism and stability analysis of water-bearing rock slopes based on particle flow simulation J Chen, J Tong, Y Rui, Y Cui, Y Pu, J Du, DB Apel Theoretical and Applied Fracture Mechanics 131, 104370, 2024 | 19 | 2024 |
| Back-analysis for initial ground stress field at a diamond mine using machine learning approaches Y Pu, DB Apel, S Prusek, A Walentek, T Cichy Natural Hazards 105, 191-203, 2021 | 18 | 2021 |
| Joint inversion of AE/MS sources and velocity with full measurements and residual estimation J Chen, J Chen, Y Rui, Y Pu Rock Mechanics and Rock Engineering 57 (9), 7371-7386, 2024 | 16 | 2024 |
| Deep and confident prediction for a laboratory earthquake Y Pu, J Chen, DB Apel Neural Computing and Applications 33 (18), 11691-11701, 2021 | 16 | 2021 |