Combining machine learning and numerical modelling for rockburst prediction.

The application of machine learning in geotechnical problems has grown rapidly. However, an issue in phenomena, such as rockburst, is the limited and imbalanced datasets, which deteriorate the reliability of machine learning algorithms. This research proposes a methodology for overcoming such issues by incorporating both the undersampling and oversampling concepts along with the use of numerical modelling. The core of the methodology is the substitution of clusters containing rockburst instances among similar materials by representative synthetic instances derived from numerical modelling on the basis of the BHP index. The models are tuned and calibrated so that their mechanical properties are aligned with the clusters' centroids and mimic their nearest neighbours in terms of stresses and rockburst intensities. Subsequently, they are subjected to a variety of stresses to fill all rockburst classes. As a result, a balanced synthetic dataset in terms of material and class participation is formed, which is used to train a Random Forest algorithm to predict rockburst. The results attained indicate the potential of the proposed methodology in enhancing the learning process and thus provide reliable long-term rockburst predictions. [ABSTRACT FROM AUTHOR]