Numerical manifold method for thermo-mechanical coupling simulation of fractured rock mass

As a calculation method based on the Galerkin variation,the numerical manifold method(NMM)adopts a double covering system,which can easily deal with discontinuous deformation problems and has a high calculation accuracy.Aiming at the thermo-mechanical(TM)coupling problem of fractured rock masses,this study uses the NMM to simulate the processes of crack initiation and propagation in a rock mass under the influence of temperature field,deduces related system equations,and proposes a penalty function method to deal with boundary conditions.Numerical examples are employed to confirm the effectiveness and high accuracy of this method.By the thermal stress analysis of a thick-walled cylinder(TWC),the simulation of cracking in the TWC under heating and cooling conditions,and the simulation of thermal cracking of the SwedishÄspöPillar Stability Experiment(APSE)rock column,the thermal stress,and TM coupling are obtained.The numerical simulation results are in good agreement with the test data and other numerical results,thus verifying the effectiveness of the NMM in dealing with thermal stress and crack propagation problems of fractured rock masses.

Rock mass quality classification based on deep learning:A feasibility study for stacked autoencoders

Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.

Genetic Mechanism of Water-Rich Landslide Considering Antecedent Rainfalls:A Case Study of Pingyikou Landslide in Three Gorges Reservoir Area

Water-rich slope,which could easily fail after prolonged or heavy rain,is very sensitive to rainfall.Pingyikou Landslide is a typical water-rich slope located in the Three Gorges Reservoir area of China.It was unstable because of the continuous rainfall that occurred from September to October 2017.To understand the deformation process and genetic mechanism of the landslide,the geomorphological features,geological characteristics,hydrological conditions,and rainfall characteristics were systematically studied by a detailed field investigation of the slope and monitoring of rainfall,water level,and displacement.In addition,the influence of different initial conditions on the stability of the slope was also studied through numerical simulation using measured rainfall data on the basis of which,the effect of antecedent rainfall on slope stability was studied by unsaturated seepage analysis method.The results show that the deformation of slope is strongly correlated with the rainfall and groundwater level,and this landslide is a typical rainfall-induced landslide.In the analysis of genetic mechanism of the same type of landslide,a maximum initial pore water pressure of -25 kPa as the initial condition is reasonable.And the antecedent rainfall has a greater effect on the stability of the slope,more than 10 days of antecedent rainfall should be considered when designing and controling the slope.