Combining machine learning and numerical modelling for rockburst prediction
Author:
Affiliation:
1. School of Mining and Metallurgical Engineering, National Technical University of Athens, Athens, Greece
Publisher
Informa UK Limited
Subject
Geotechnical Engineering and Engineering Geology
Link
https://www.tandfonline.com/doi/pdf/10.1080/17486025.2023.2207546
Reference39 articles.
1. Statistical assessment of rock burst potential and contributions of considered predictor variables in the task
2. Generalized crack initiation and crack damage stress thresholds of brittle rock masses near underground excavations
3. Bagged Ensemble of Gaussian Process Classifiers for Assessing Rockburst Damage Potential with an Imbalanced Dataset
4. The 2003 Canadian Geotechnical Colloquium: Mechanistic interpretation and practical application of damage and spalling prediction criteria for deep tunnelling
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