Improved rotational hardening rule for cohesive soils and definition of inherent anisotropy

Improved, microfabric‐inspired rotational hardening rules for the plastic potential and bounding surfaces associated with the generalized bounding surface model for cohesive soils are presented. These hardening rules include 2 new functions, f_η and , that improve the simulation of anisotropically consolidated cohesive soils. Three model parameters are associated with the improved hardening rules. A detailed procedure for obtaining suitable values for these parameters is presented. The first 2 parameters affect the simulation of constant stress ratio loading where, because of the presence of f_η, the third parameter is inactive. The second new function, , accelerates the rotation of the plastic potential and bounding surfaces during shearing, which is particularly important for overconsolidated soils tested in extension. This paper also describes the proper manner in which to define the inherent anisotropy. This seemingly straightforward test has rarely been discussed in sufficient detail.     

Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables

ABSTRACT

The potential of different models – deep echo state network (DeepESN), extreme learning machine (ELM), extra tree (ET), and regression tree (RT) – in estimating dew point temperature by using meteorological variables is investigated. The variables consist of daily records of average air temperature, atmospheric pressure, relative humidity, wind speed, solar radiation, and dew point temperature (T_dew) from Seoul and Incheon stations, Republic of Korea. Evaluation of the model performance shows that the models with five and three-input variables yielded better accuracy than the other models in these two stations, respectively. In terms of root-mean-square error, there was significant increase in accuracy when using the DeepESN model compared to the ELM (18%), ET (58%), and RT (64%) models at Seoul station and the ELM (12%), ET (23%), and RT (49%) models at Incheon. The results show that the proposed DeepESN model performed better than the other models in forecasting T_dew values.     

Effect of seismic waves on the hydro-mechanical properties of fractured rock masses

The transmission of seismic waves in a particular region may influence the hydraulic properties of a rock mass, including permeability, which is one of the most important. To determine the effect of a seismic wave on the hydraulic behavior of a fractured rock mass, systematic numerical modeling was conducted. A number of discrete fracture network(DFN) models with a size of 20 m × 20 m were used as geometrical bases, and a discrete element method(DEM) was employed as a numerical simulation tool. Three different boundary conditions without(Type Ⅰ) and with static(Type Ⅱ) and dynamic(Type Ⅲ) loading were performed on the models, and then their permeability was calculated. The results showed that permeability in Type Ⅲ models was respectively 62.7% and 44.2% higher than in Type I and Type Ⅱ models. This study indicates that seismic waves can affect deep earth, and, according to the results, seismic waves increase the permeability and change the flow rate patterns in a fractured rock mass.     

Stability Analysis and Optimal Design of Ultimate Slope of an Open Pit Mine: A Case Study

Geotechnical and Geological Engineering - To avoid over-tailing and limit the risk of failure in open-pit mines, choosing an ideal and stable slope is critical. The major goal of this study was to...