Rockburst assessment in deep geotechnical conditions using true-triaxial tests and data-driven approaches

Deep underground excavations in mining and civil engineering are subjected to high in-situ stresses which can cause rockburst. Rockburst is an instantaneous release of a large amount of strain energy stored in rockmass that can lead to injuries, deaths, and damage to infrastructures. Many studies have been done regarding rockburst, however, there is no practical model to predict the stress level that rockburst occurs (i.e. maximum rockburst stress) and its related risk (i.e. rockburst risk index) based on real rockburst tests, and the main rock mechanical properties. In this study, a comprehensive database of true-triaxial unloading tests on rocks having a wide range of properties was compiled. The agglomerative hierarchical clustering (AHC) analysis was carried out on the original database to evaluate the presence of natural groups and outliers. Then, the stepwise selection and elimination (SSE) procedure were employed for dimension reduction of the problem and identifying the most influential attributes on rockburst parameters. Afterward, two robust non-linear algorithms, including gene expression programming (GEP) and classification and regression tree (CART) were used to develop the predictive models for rockburst maximum stress and its risk index. The validation verification of the proposed models using several indices proved the high prediction performance of the developed non-linear models. Finally, a parametric analysis was carried out to evaluate the influence of each input parameter on the corresponding output. The proposed models in this study are practical and do not require any presupposition about rockburst mechanism, which makes them be used easily in practice by engineers at the design and progress stages of the underground projects.