SVD enabled data augmentation for machine learning based surrogate modeling of non-linear structures

Abstract

The computationally expensive estimation of engineering demand parameters (EDPs) via finite element (FE) models, while considering earthquake and material parameter uncertainty limits the use of the Performance Based Earthquake Engineering framework. Attempts have been made to substitute FE models with surrogate models, however, most of these models are a function of building parameters only. This necessitates re-training for earthquakes not previously seen by the surrogate. In this paper, the authors propose a machine learning based surrogate model framework, which considers both these uncertainties in order to predict for unseen earthquakes. Accordingly, earthquakes are characterized by their projections on an orthonormal basis, computed using SVD of a representative ground motion suite. This enables one to generate varieties of earthquakes by randomly sampling these weights and multiplying them with the basis, resulting in a large data set that is needed to train machine learning models. The weights along with the constitutive parameters serve as inputs to a machine learning model with EDPs as the desired output. Four competing machine learning models were tested and it was observed that a deep neural network (DNN) gave the most accurate prediction. The framework is validated by using it to successfully predict the peak response of one-story and three-story shear frame buildings represented using nonlinear spring–mass–damper systems, subjected to unseen far-field ground motions.