Identification of Nonlinear Soil Properties from Downhole Array Data Using a Bayesian Model Updating Approach

Abstract

An accurate seismic response simulation of civil structures requires accounting for the nonlinear soil response behavior. This, in turn, requires understanding the nonlinear material behavior of in situ soils under earthquake excitations. System identification methods applied to data recorded during earthquakes provide an opportunity to identify the nonlinear material properties of in situ soils. In this study, we use a Bayesian inference framework for nonlinear model updating to estimate the nonlinear soil properties from recorded downhole array data. For this purpose, a one-dimensional finite element model of the geotechnical site with nonlinear soil material constitutive model is updated to estimate the parameters of the soil model as well as the input excitations, including incident, bedrock, or within motions. The seismic inversion method is first verified by using several synthetic case studies. It is then validated by using measurements from a centrifuge test and with data recorded at the Lotung experimental site in Taiwan. The site inversion method is then applied to the Benicia–Martinez geotechnical array in California, using the seismic data recorded during the 2014 South Napa earthquake. The results show the promising application of the proposed seismic inversion approach using Bayesian model updating to identify the nonlinear material parameters of in situ soil by using recorded downhole array data.

Convolutional neural network for identifying effective seismic force at a DRM layer for rapid reconstruction of SH ground motions

“…Bayesian estimation method has also been studied for the FE model updating of the nonlinear soil properties in a soil column and the characterization of the vertically propagating incoming seismic wave toward the column. 9 Such a Bayesian FE model estimation method was extended to a problem where the structural properties of an instrumented building and its foundation input motion at the nodal points of the foundation slab are identified during a seismic event. 10 The nodal points of the foundation slab are modeled with 3-axis linear springs and 3-axis linear viscous dampers.…”

“…Output‐only blind identification methods were investigated to identify the transfer functions of soil columns and vertically propagating incident waves, accounting for geophysical uncertainty by analyzing measured ground motion signals from multiple stations, but only vertically propagating incident waves have been considered. Bayesian estimation method has also been studied for the FE model updating of the nonlinear soil properties in a soil column and the characterization of the vertically propagating incoming seismic wave toward the column 9 . Such a Bayesian FE model estimation method was extended to a problem where the structural properties of an instrumented building and its foundation input motion at the nodal points of the foundation slab are identified during a seismic event 10 .…”

“…Engineers can use this technique to make precise estimates of the impact of earthquakes on soils and critical structures, identifying weak spots or areas that may be at risk of damage. Current practices, which utilize ground measurements for estimating the seismic waves impinging a domain of interest, as a prior step to the reconstruction of the wave responses in the domain, typically rely on (i) the deconvolution procedure in a soil column, [1][2][3][4] (ii) the seismic source identification in a large-scale domain, 5,6 (iii) output only blind identification, 7,8 (iv) Bayesian finite element (FE) model updating, 9,10 and (v) multiwavefield inversion. 11 When it comes to deconvolution, the technique is reliable only in scenarios where a soil column is horizontally layered, and the seismic waves propagate vertically through a one-dimensional (1D) medium.…”