Although advanced modeling techniques such as the finite element method (FEM) have been used successfully in dynamic site response analysis, the high computational expense has hindered the incorporation of input parameter uncertainty in such analysis. Thus, when the variation of the peak ground acceleration (PGA) at the ground surface, which is the outcome of the site response analysis, has to be evaluated in the face of uncertainty, a surrogate model such as Response Surface Method (RSM) model is often used in lieu of the FEM model. In this paper, the RSM surrogate model was implemented in the context of seismic site response analysis to evaluate the variation (or uncertainty) of the PGA due to the propagation of input parameter uncertainty. The engineering implication of the variation of the surface PGA is significant as this knowledge is required for such tasks as reliability analysis of soil liquefaction and probabilistic seismic risk analysis. To derive the RSM model specifically for a site response analysis, a parametric study of the dynamic site responses using FEM code ABAQUS with the modified Davidenkov soil constitutive model implemented as a user‐defined material subroutine is first conducted. The input parameters, including the soil profile, soil properties, and input ground motion, in a typical dynamic site response analysis are then characterized and screened for their suitability to be included in the RSM model. For a given site response problem in the face of uncertainty, representative “samples” are taken for site response analysis using ABAQUS, and the analyses are carried out and finally the response surface is constructed using the outcomes of the ABAQUS numerical experimentations. The accuracy of the developed RSM model is then validated using the results of ABAQUS on cases not used in the development of the RSM model. Once the RSM model is deemed satisfactory, the reliability‐based formulation for evaluating the mean and standard deviation of the surface PGA is established. Example is provided to illustrate the RSM approach for dynamic site response analysis in the face of input parameter uncertainty.

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It has been recognized that the soil profile and the associated parameters are usually not known with certainty in probabilistic seismic hazard analysis, however, little is known about the influence of site conditions on the propagation of uncertainty. This work aims to analyze and quantify the propagation of soil property uncertainties in site response analysis under different site conditions using a recently proposed DCZ constitutive model. Sensitivity analysis is performed to determine the most important parameter, which is regarded as a random variable in the subsequent uncertainty analysis. A Monte Carlo simulation procedure is applied to quantify the uncertainty propagation caused by the random variable.Detailed case studies involving multiple site conditions are presented to quantify the impacts of soil property variability under different site conditions in site responses. This study has found that variation in site response strongly depends on the shear wave velocity compared to other parameters. For softer soil, the PGA values at the ground surface show more dispersion (i.e., uncertainties) compared to stiffer soil. This indicates that for improving the accuracy of site response analysis and earthquake disaster prediction, it is necessary to operate probabilistic dynamic site response analysis rather than deterministic analysis, especially for soft soil.