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First, generate a 2DRdF by interpolating 1D RdF simulations is not correct even if u assume orthotropic conditions. Simply u can not model/preserve the spatial structure following the horizontal direction with the approach that u proposed. Again, I advise you to start by 1D case to understand the basis that u're doing. For the 1D case, u can generate the RdF by Cholesky decomposition, the simplest approach.

Then, to my best knowledge OpenSees doesn't have implemented any module to simulate RdF. It has a reliability analysis module based on a classic Random variable modelling framework to deal with uncertainties. Therefore you have to generate yourself the RdF and then map to the grid. Literature in this field is extensive (i.e. midpoint method). I also advise to u work through the Python interpreter (OpenSeesPy) to further automate your workflow.

KR

Hi Sebastian,

It seems to me that you want to deal with soil variability by modeling soil properties as a random field.

I think that the best way to start is by the simplest case, a 1D model. For example, one may assume that Vs varies linearly with depth. You can then replicate the same steps given in the examples to model the soil as continuum, but the key is to create "several thin layers". Basically the idea is to map the Vs(z) onto the center of each layer to reproduce this linear depth dependent Vs profile.

After that, you may start to look at the 2D and finally 3D problem. If you really want to represent as RdF the soil properties, you want to learn about Gaussiand RdF and RdF generators to perform simulations in other to create plausible representations over the space of the soil properties. But that's is another story and it's not related with OpenSees or any FE framework.

Gl

I've recently discovered the implementation of PFEM in OpenSees to deal with FSI problems. After reading the documentation and the related articles, I was wondering if it's possible to generate wave train through PFEM simulations in OpenSees.

Thanks in advance for your support