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

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This study estimates nonlinear soil properties using Bayesian inference and downhole array data. The method accurately identifies soil behavior for improved seismic response simulations of civil structures.

Keywords:
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Area of Science:

  • Geotechnical Engineering
  • Earthquake Engineering
  • Computational Geosciences

Background:

  • Accurate seismic response simulation of civil structures necessitates understanding nonlinear soil behavior under earthquake excitations.
  • System identification from earthquake recordings offers a pathway to determine in situ soil nonlinear material properties.

Purpose of the Study:

  • To develop and validate a Bayesian inference framework for nonlinear model updating to estimate in situ soil properties from downhole array data.
  • To apply the seismic inversion method to real-world data for identifying nonlinear soil parameters.

Main Methods:

  • Utilized a Bayesian inference framework for nonlinear model updating.
  • Employed a one-dimensional finite element model with a nonlinear soil constitutive model.
  • Estimated soil model parameters and input excitations (incident, bedrock, or within motions).
  • Verified the seismic inversion method with synthetic data, centrifuge tests, and the Lotung experimental site data.

Main Results:

  • The seismic inversion method was successfully verified and validated using diverse datasets.
  • Application to the Benicia-Martinez geotechnical array during the 2014 South Napa earthquake demonstrated effectiveness.
  • The approach successfully identified nonlinear material parameters of in situ soil.

Conclusions:

  • The proposed seismic inversion approach using Bayesian model updating is a promising tool for identifying nonlinear soil material parameters.
  • Accurate characterization of nonlinear soil behavior is crucial for reliable seismic response simulations of civil infrastructure.