Surrogate models are growing in popularity within the earthquake engineering community because of their ability to increase the efficiency of computationally intensive tasks. This article examines the design of computer experiments (DoCE) for the purpose of developing seismic surrogate models. Two categories of DoCE approaches are discussed while underscoring the benefits and drawbacks of specific methods. Further insight is provided through an illustrative case study that develops surrogate models to predict the median collapse capacity and expected annual losses in single-family woodframe buildings with cripple walls. The implications of the chosen DoCE method on the predictive performance and efficiency (in terms of the required number of explicit simulations) of the associated surrogate models are closely examined. The results show that both factorial and Latin hypercube designs (LHDs) do not perform well when different approaches are used to generate the training and testing sets (i.e. out-of-design-type testing). An efficient hybrid design that combines an orthogonal array-based composite design with a small number of LHD samples is shown to produce a surrogate model with superior predictive performance.

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