Reservoir induced earthquakes analyzed via radial basis function networks

Estimation of the magnitude of reservoir induced seismicity is essential for seismic risk analysis of dam sites. Different geological and empirical methods dealing with the mechanism or magnitude of such earthquakes are available in the literature. In this study, a method based on an artificial neural network utilizing radial basis functions (RBF network) was employed to analyze the problem. The network has only two input neurons, one representing the maximum depth of the reservoir and the other being a comprehensive parameter representing reservoir geometry. Magnitudes of the induced earthquakes predicted using the RBF network were compared with the actual recorded data. Compared with the conventional statistical approach, the proposed method gives a better prediction, both in terms of coefficients of correlation and error rates.     

Effect of Particles Shape on the Hydraulic Conductivity of Stokesian Flow in Granular Materials

Geotechnical and Geological Engineering - The hydraulic conductivity of a granular porous medium depends on parameters such as the porosity, particles shape and fluid viscosity. Although other...     

Cyclic liquefaction strength of sands

Through an energy approach, a model is proposed to predict the cyclic liquefaction strength of saturated sands in terms of their static shear strengths. Plots of cyclic liquefaction strength versus relative density and also versus modified standard penetration resistance are presented for various uniformity coefficients and different numbers of stress cycles. The predicted cyclic liquefaction strength values are converted to cyclic stress ratios and compare favourably with Seed's empirical correlations.     

Determination of unsaturated hydraulic conductivity of sandy soils: a new pore network approach

Acta Geotechnica - Hydraulic conductivity is one of the most important characteristics of unsaturated soils. Its determination is essential for modeling various phenomena of interest such as...     

Modelling the mechanical behaviour of unsaturated soils using a genetic algorithm-based neural network

Modelling the mechanical behaviour of unsaturated soils has been the subject of many research works in the past few decades. A number of constitutive models have been developed to describe the complex behaviour of unsaturated soils. Despite the significant advances in the constitutive theories for unsaturated soils, none of the existing models can completely describe the various aspects of the real behaviour of unsaturated soils. In this paper, a new unified approach is presented, based on the integration of a neural network and a genetic algorithm, for the modelling of unsaturated soils. In the proposed approach, a genetic algorithm was used to optimise the weights of the neural network. A three-layer sequential architecture was chosen for the neural network. The network had eight input neurons, five neurons in the hidden layer and three neurons in the output layer. The eight input neurons represented the initial gravimetric water content, initial dry density, degree of saturation, net mean stress with respect to pore-air pressure, axial strain, deviatoric stress, soil suction and volumetric strain, and the three neurons in the output layer represented the deviatoric stress, suction and volumetric strain at the end of each increment. The network was trained and tested using a database that included results from a comprehensive set of triaxial tests on unsaturated soils from the literature. The predictions of the proposed model were compared with the experimental results. The comparison of the results indicates that the proposed approach was accurate and robust in representing the mechanical behaviour of unsaturated soils.     

Bearing capacity factor, <Emphasis Type="Italic">N</Emphasis><Subscript><Emphasis Type="Italic">γ</Emphasis></Subscript>, for unsaturated soils by ZEL method

Bearing capacity of foundations is often determined for saturated state of the soil, regarding its simple and conservative results. This assumption, however, results in very uneconomic and overconservative design for a wide range of climates in the world. In this paper, plasticity equations were employed and extended for unsaturated soils to establish a theoretical approach to investigate the bearing capacity of unsaturated soils. It is achieved by combining the concept of effective stress and plasticity equations in terms of effective stress in unsaturated soils. The advantage of Bishop’s (4) effective stress concept was employed to simplify the equations. The equations were then transformed onto the zero extension lines directions to generalize this method for both associative and non-associative problems by which both stress and velocity field can be determined for unsaturated soils. A computer code was also developed to solve the relatively complex plasticity equations for a wide range of soil friction angles and matric suctions to compute the corresponding bearing capacity factor, N _γ , for strip foundations with smooth and rough base. This factor seems to be one of the major contributors in the bearing capacity of shallow foundations. The results have been presented in design charts and theoretical equations.