  The computationally expensive estimation of engineering demand parameters
(EDPs) via finite element (FE) models, while considering earthquake and
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 large
varieties of earthquakes by randomly sampling these weights and multiplying
them with the basis. 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 buildings represented using stick models, subjected to unseen
far-field ground motions.
