Abstract
The first step for learning any calculation code for seismic response analysis is an adequate understanding of how to properly set the boundary conditions and the properties of the soil model at the initial stage, i.e., before the shaking event. To pursue this aim, nine different computer codes suitable for seismic response analyses of soil profiles have been reviewed. An ideal twenty-meter soil column with visco-elastic linear behavior, subjected to a pulse-like input motion, has been reproduced with the different codes with the scope to practically show the differences and peculiarities of each of them. In the definition of the soil properties in the small-strain range, special attention has been devoted to the definition of the damping ratio, usually defined in non-linear codes as viscous damping according to the Rayleigh formulation. This simple one-dimensional exercise has been considered as a useful benchmark for verifying the rightness of the application of the boundary conditions and setting the initial soil properties. The same analysis can be easily reproduced by beginner users and, therefore, constitutes a starting point in the learning phase of new and/or more sophisticated 2D and 3D calculation codes for seismic site response analysis.
Highlights
Geosciences 2022, 12, 83. https://doi.Site effects observed after strong earthquakes are ascribed to the phenomena of amplification of ground motions due to local soil conditions, both stratigraphic and morphological, superficial and buried
11 of of theofmaximum values of acceleration, shearshear stress and and shear strain attained during the files the maximum values of acceleration, stress shear strain attained during analyses
As when the initial and boundary conditions are correctly set,thethe results show that all codes lead to the same soil column response in terms of results show that all codes lead to the same soil column response in terms of vertical vertical profiles, no appreciable differences
Summary
Site effects observed after strong earthquakes are ascribed to the phenomena of amplification of ground motions due to local soil conditions, both stratigraphic and morphological, superficial and buried. The assessment of these site effects, carried out through Site Response Analyses (SRA) able to predict the ground surface motion, can be aimed both to design/solve problems at the scale of the single structure [1,2,3], and to support studies at a large scale [4,5,6] such as, for example, the third level seismic microzonation [7,8]. The recent trend to execute always more sophisticated and complex dynamic analyses, inside artificial neural networks (or machine learning techniques in general) and to generate huge datasets of simulations, furtherly exacerbates the need for full control on the adopted models and codes
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