S. E. Mirsalari; M. Fatehi Marji; J. Gholamnejad; M. Najafi
Abstract
Analysis of the stresses, displacements, and horizontal strains of the ground subsidence due to underground excavation in rocks can be accomplished by means of a hybridized higher order indirect boundary element/finite difference (BE/FD) formulation. A semi-infinite displacement discontinuity field is ...
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Analysis of the stresses, displacements, and horizontal strains of the ground subsidence due to underground excavation in rocks can be accomplished by means of a hybridized higher order indirect boundary element/finite difference (BE/FD) formulation. A semi-infinite displacement discontinuity field is discretized (numerically) using the cubic displacement discontinuity elements (i.e. each higher order element is divided into four sub-elements bearing a cubic variation in the displacement discontinuities). Then the classical finite difference formulation (i.e. the backward, central, and forward finite difference formulations) is hybridized using the boundary element formulation, enabling us to obtain the nodal tangential stresses and horizontal strains along the elements. Several example problems are solved numerically, and the results obtained are then compared with their corresponding results available in the literature. These comparisons show the effectiveness and validness of the proposed method. A classical practical problem is also used to verify the applicability of the hybridized method.
Javad Gholamnejad; HamidReza Bahaaddini; Morteza Rastegar
Abstract
Static deformation modulus is recognized as one of the most important parameters governing the behavior of rock masses. Predictive models for the mechanical properties of rock masses have been used in rock engineering because direct measurement of the properties is difficult due to time and cost constraints. ...
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Static deformation modulus is recognized as one of the most important parameters governing the behavior of rock masses. Predictive models for the mechanical properties of rock masses have been used in rock engineering because direct measurement of the properties is difficult due to time and cost constraints. In this method the deformation modulus is estimated indirectly from classification systems. This paper presents the results of a study into the application of Artificial Neural Networks (ANN) technique and Regression models for estimation of the deformation modulus of rock masses. A database, including 225 actual measured deformation modulus, Uniaxial Compressive Strengths of the rock (UCS), and Rock Mass Rating (RMR) was established. Data collected from different projects. For predicting Em by regression, a nonlinear regression method was chosen. This model showed the coefficient correlation of 0.751 and mean absolute percentage error (MAPE) of 9.911%. Also a three-layer ANN was found to be optimum, with an architecture of two neurons in the input layer, four neurons in the hidden layer and one neuron in the output layer. The correlation coefficient determined for deformation modulus predicted by the ANN was 0.786 and the quantity of MAPE was 6.324%. With respect to the results obtained from two models, the ANN technique was shown to be better than the regression model because of its higher accuracy.
