A methodology for estimation of site-specific nonlinear dynamic soil behaviour using vertical downhole arrays

Abstract

System identification (SI) techniques have been widely used in Geotechnical Engineering to understand the characteristics of soils. In this study, system identification techniques with and without using a constitutive model were examined using three downhole array sites in California, US. Shear wave velocities (V_S) and maximum shear moduli (G_max) of soils were obtained using SI techniques without using a constitutive model and low-amplitude earthquake records. Average shear moduli and average damping ratios of soil layers were estimated through SI technique using a linear constitutive model and relatively high amplitude records. Finally, a practical methodology was proposed for predicting site-specific shear modulus degradation (G/G_max) and damping curves (ξ) using nonlinear constitutive models and high amplitude earthquake data recorded in some arrays. The proposed methodology predicts site-specific dynamic characteristics by using the recorded earthquake data and iterating the reference strain (γ_r) in the nonlinear constitutive model. From the results, it can be inferred that average shear modulus values estimated using the linear constitutive model might not predict the dynamic soil behaviour properly for high amplitude earthquakes. However, the acceleration time histories obtained using the site-specific G/G_max and ξ curves predicted by the proposed methodology using nonlinear constitutive model were in good agreement with the recorded acceleration data.

Keywords:

• System identification
• constitutive soil models
• downhole array
• site-specific dynamic soil properties
• reference strain

Acknowledgements

The authors would like to acknowledge the networks that supply strong motion data recorded at the downhole array sites. The strong motion data recorded by Engineering Data Center (EDC) (operated by the Center for Engineering Strong Motion Data (CESMD) to provide public access) and NEES @ UCSB were used in this research. The networks or agencies providing the data used in this paper are the California Strong Motion Instrumentation Program (CSMIP) and the USGS National Strong Motion Project (NSMP).