W.Y. Xu

Prediction of rock burst classification using the technique of cloud models with attribution weight

Rock burst is one of the common failures in hard rock mining and civil construction. This study focuses on the prediction of rock burst classification with case instances using cloud models and attribution weight. First, cloud models are introduced briefly related to the rock burst classification problem. Then, the attribution weight method is presented to quantify the contribution of each rock burst indicator for classification. The approach is implemented to predict the classes of rock burst intensity for the 164 rock burst instances collected. The clustering figures are generated by cloud models for each rock burst class. The computed weight values of the indicators show that the stress ratio

Comprehensive Stability Evaluation of Rock Slope Using the Cloud Model-Based Approach

Ts = sigma_{theta } /sigma_{c}

Stability evaluation model for high rock slope based on element extension theory

Ts=σθ/σc is the most vulnerable parameter and the elastic strain energy storage index Wet and the brittleness factor

Experimental studies on hydro-mechanical properties of metamorphic rock under hydraulic pressures

B = sigma_{c} /sigma_{t}

Prediction of elastic compressibility of rock material with soft computing techniques

B=σc/σt take the second and third place, respectively, contributing to the rock burst classification. Besides, the predictive performance of the strategy introduced in this study is compared with that of some empirical methods, the regression analysis, the neural networks and support vector machines. The results turn out that cloud models perform better than the empirical methods and regression analysis and have superior generalization ability than the neural networks in modelling the rock burst cases. Hence, cloud models are feasible and applicable for prediction of rock burst classification. Finally, different models with varying indicators are investigated to validate the parameter sensitivity results obtained by cloud clustering analysis and regression analysis in context to rock burst classification.

Permeability Evolution of Granite Gneiss During Triaxial Creep Tests

This work is devoted to characterization of the deformation and strength properties of cataclastic sandstones. Before conducting mechanical tests, the physical properties were first examined. These sandstones are characterized by a loose damaged microstructure and poorly cemented contacts. Then, a series of mechanical tests including hydrostatic, uniaxial, and triaxial compression tests were performed to study the mechanical strength and deformation of the sandstones. The results obtained show nonlinear stress–strain responses. The initial microcracks are closed at hydrostatic stress of 2.6xa0MPa, and the uniaxial compressive strength is about 0.98xa0MPa. Under triaxial compression, there is a clear transition from volumetric compressibility to dilatancy and a strong dependency on confining pressure. Based on the experimental evidence, an elastoplastic model is proposed using a linear yield function and a nonassociated plastic potential. There is good agreement between numerical results and experimental data.