A borehole array data–based approach for conducting 1D site response analyses II: Accounting for modeling errors

Abstract

Site response estimates from one-dimensional (1D) site response analyses (SRAs) carry inaccuracies due to modeling and parametric errors. Modeling errors are due to the condensation of the three-dimensional (3D) wave propagation phenomenon to the vertical propagation of a horizontally polarized wave through a soil column, and parametric errors are due to the incomplete knowledge of the distributions of soil parameters, leading to the selection of nonoptimal input parameters for a site of interest. While parametric errors are traditionally handled using different soil parameters (e.g. alternative shear-wave velocity profiles), modeling errors are generally neglected. This paper proposes an approach for conducting linear elastic 1D SRAs to improve site response predictions and account for modeling errors. First, ground-motion data from borehole array sites are collected, processed, and screened for appropriateness (e.g. expected shear strains lower than 0.01%, signal-to-noise ratio higher than 3). Second, 1D SRA predictions in terms of transfer functions and amplification factors are compared against observations, and the discrepancies are quantified as residuals. Finally, the residuals are partitioned into a model bias term [Formula: see text], a site term [Formula: see text] with standard deviation [Formula: see text], and a event- and site-specific remaining residual [Formula: see text] with standard deviation [Formula: see text]. Values for [Formula: see text] and [Formula: see text] for forward predictions are recommended. The sensitivity of the site response residuals to region, site type (1D- or 3D-like), and the applicability of findings to outcropping applications are discussed, and an example application for a hypothetical project site is presented.

A borehole array data–based approach for conducting 1D site response analyses I: Damping and VS randomization

“…The second part consists of the quantification of the 1D SRA remaining errors such that they can be considered in forward site response predictions. This article focuses on the first part, and the companion paper (Pretell et al, 2023) describes the second part.…”