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1. #EQUILIBRIUM 3D SOFTWARE INSTALL#
2. #EQUILIBRIUM 3D SOFTWARE MANUAL#
3. #EQUILIBRIUM 3D SOFTWARE FULL#
4. #EQUILIBRIUM 3D SOFTWARE SOFTWARE#

#EQUILIBRIUM 3D SOFTWARE INSTALL#

#EQUILIBRIUM 3D SOFTWARE SOFTWARE#

3D variably saturated pressure heads or pore pressures, including the effects of unsaturated suction stressesĭownload complete installation package including software and documentation (recommended).Įasy-to-install software packages for Windows or Macintosh operating systems, includes Scoops3D, Scoops3D-i, and Scoops3D manual:.

#EQUILIBRIUM 3D SOFTWARE FULL#

#EQUILIBRIUM 3D SOFTWARE MANUAL#

The user's manual includes: the theoretical basis for the slope-stability analysis, requirements for constructing a 3D domain, a detailed operational guide and input/output file specifications, practical considerations for conducting an analysis, results of verification tests, and multiple examples illustrating the capabilities of Scoops3D. It provides the least-stable potential landslide for each DEM cell in the landscape, as well the associated volumes and (or) areas. For each potential landslide, Scoops3D assesses the stability of a rotational, spherical slip surface encompassing many DEM cells. The program uses a three-dimensional (3D) method of columns limit-equilibrium analysis to assess the stability of many potential landslides (typically millions) within a user-defined size range. Scoops3D evaluates slope stability throughout a digital landscape represented by a digital elevation model (DEM). (From Scoops3D user’s manual.) (Public domain.) Numerical and experimental examples are presented to show that the developed ECNN can faithfully extract the stress corresponding to local strain, model the multiaxial stress-strain constitutive behavior of hyperelastic materials, and surrogate equation-based conventional constitutive models of hyperelastic materials.Model of 3D slope stability of a volcano edifice computed using Scoops3D software. Moreover, the ECNN is unsupervised by noting that training is not against the output stress. The equation of equilibrium is embedded in the architecture of the ECNN as a constraint to implicitly establish the correspondence between the internal variables and the stress components and, consequently, the constitutive stress–strain relationship. Therefore, a large database can be easily generated with a single non-uniformly deformed specimen with spatially dependent strain. Only strain and externally applied force are taken as input for training while the hard-to-measure stress is considered as internal variables. In this paper, an equilibrium-based convolution neural network (ECNN) is proposed to model the constitutive behavior of hyperelastic materials. Moreover, most developed approaches are based upon supervised learning and ignore known physical laws, resulting in limited interpretability and generalization.

However, it is usually formidable to experimentally obtain such curves by noting that, although strain can be easily measured, stress can only be extracted for limited specimens with a simple and uniform stress field upon loading. In these methods, a large database of multi-axial stress versus strain curves is indispensable for training and cross-validation. Recently, various machine learning methods have been developed to model the stress-strain constitutive behavior of materials.