Loading…

An efficient algorithm for sampling the shear-modulus reduction curve in the context of wave propagation using the elastoplastic Iwan model

SUMMARY The elastoplastic Iwan model has been used since the end of the 1970s to simulate nonlinear soil behaviour in seismic wave propagation. In this work, we present an automatic algorithm to efficiently sample the shear-modulus reduction curve in function of shear deformation, which constitutes...

Full description

Saved in:
Bibliographic Details
Published in:Geophysical journal international 2022-03, Vol.228 (3), p.1907-1917
Main Authors: Chabot, S, Mercerat, E D, Glinsky, N, Bonilla, L F
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:SUMMARY The elastoplastic Iwan model has been used since the end of the 1970s to simulate nonlinear soil behaviour in seismic wave propagation. In this work, we present an automatic algorithm to efficiently sample the shear-modulus reduction curve in function of shear deformation, which constitutes the exclusive ingredient of the elastoplastic model. This model requires the data from the shear- modulus reduction as a function of shear deformation, which are readily available in the literature and from specific laboratory tests. The method involves a discretization and interpolation of these data to be used. The quality of the solution depends on the number of interpolated points. However, a larger number of them produce an increase of the computational time. To overcome this, we present an automatic algorithm to efficiently sample the shear-modulus reduction curve. We numerically prove that the chosen discretization of the curve has a strong impact on the calculation load, in addition to the well-known dependence on the input motion amplitude level. Two tests of nonlinear wave propagation in 1-D and 3-D media show the clear gain in computation time when using the proposed automatic sampling algorithm.
ISSN:0956-540X
1365-246X
DOI:10.1093/gji/ggab431