Study on the Influence of Rainfall Infiltration on the High rock Slope of Open-pit Mine Stability using a New Nonlinear Strength Reduction Method

Due to long-term mining, a series of high and steep rock slopes have been formed in the open-pit mine. For high rock slopes, rainfall infiltration is the main cause of landslide. Therefore, the stability analysis of high rock slope under rainfall has become a key issue in the open-pit mine engineering. In this work, aiming at the high stress condition of high rock slope, the instantaneous internal friction angle and instantaneous cohesion of rock mass under different stress states are deduced, and the a nonlinear strength reduction method for high rock slope is established according to the relationship between normal stress and shear stress of rock mass under the Hoke-Brown criterion. The numerical calculation results show that the factor of safety (FOS) for high rock slope calculated by the proposed method is more reasonable. Taking the southwest slope of Dagushan Iron Mine as the research background, the safety factors of high rock slope under different rainfall conditions are calculated by COMSOL Multiphysics. And the stability analysis of high rock slope in open-pit mine under rainfall are carried out.

A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data

The blast-induced damage of a high rock slope is directly related to construction safety and the operation performance of the slope. Approaches currently used to measure and predict the blast-induced damage are time-consuming and costly. A Bayesian approach was proposed to predict the blast-induced damage of high rock slopes using vibration and sonic data. The relationship between the blast-induced damage and the natural frequency of the rock mass was firstly developed. Based on the developed relationship, specific procedures of the Bayesian approach were then illustrated. Finally, the proposed approach was used to predict the blast-induced damage of the rock slope at the Baihetan Hydropower Station. The results showed that the damage depth representing the blast-induced damage is proportional to the change in the natural frequency. The first step of the approach is establishing a predictive model by undertaking Bayesian linear regression, and the second step is predicting the damage depth for the next bench blasting by inputting the change rate in the natural frequency into the predictive model. Probabilities of predicted results being below corresponding observations are all above 0.85. The approach can make the best of observations and includes uncertainty in predicted results.