Nonlinear Reduced Order Modelling of Soil Structure Interaction Effects via LSTM and Autoencoder Neural Networks

TL;DR

This paper presents a novel nonlinear reduced order modeling approach using Autoencoder and LSTM neural networks to efficiently simulate soil-structure interaction effects in wind turbines, achieving high accuracy with reduced computational cost.

Contribution

The study introduces a new nonlinear reduced order modeling technique combining Autoencoder and LSTM networks for soil-structure interaction problems, demonstrating its effectiveness on wind turbine monopile simulations.

Findings

ROM achieved high fidelity compared to full simulations

Significant reduction in computational time

Applicable to nonlinear soil-structure interaction modeling

Abstract

In the field of structural health monitoring (SHM), inverse problems which require repeated analyses are common. With the increase in the use of nonlinear models, the development of nonlinear reduced order modelling techniques is of paramount interest. Of considerable research interest, is the use of flexible and scalable machine learning methods which can learn to approximate the behaviour of nonlinear dynamic systems using input and output data. One such nonlinear system of interest, in the context of wind turbine structures, is the soil structure interaction (SSI) problem. Soil demonstrates strongly nonlinear behaviour with regards to its restoring force and has been shown to considerably influence the dynamic response of wind turbine structures. In this work, we demonstrate the application of a recently developed nonlinear reduced order modelling method, which leverages Autoencoder and LSTM neural networks, to a nonlinear soil structure interaction problem of a wind turbine monopile subject to realistic loading at the seabed level. The accuracy and efficiency of the methodology is compared to full order simulations carried out using Abaqus. The ROM was shown to have good fidelity and a considerable reduction in computational time for the system considered.