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In geotechnical site characterization, Cone Penetration Testing (CPT) is a fundamental method for evaluating subsurface conditions of granular materials such as sands, silts, and non-plastic tailings. This study advances CPT simulations by incorporating the fabric anisotropy variable A into the SANISAND-F model and utilizing the Material Point Method (MPM), with a specific focus on the role of evolving fabric within an anisotropic critical state framework. The objective is to deepen the understanding of how the evolving fabric of soils influences macroscopic site characterization outcomes. Through carefully controlled initial conditions, including void ratio and confining pressure, the study aims to demonstrate the impact of fabric anisotropy on CPT resistance measurements. Assessment of the evolution of material state based on the key constitutive ingredient of the model allows for explaining the reason behind the respective values of cone tip resistance observed from the CPT simulations, considering fabric anisotropy and the anisotropic critical state framework. This approach enhances the modeling of this site characterization method, providing a more comprehensive framework for interpreting soil mechanical behavior and enhancing predictive modeling capabilities
 
In geotechnical site characterization, Cone Penetration Testing (CPT) is a fundamental method for evaluating subsurface conditions of granular materials such as sands, silts, and non-plastic tailings. This study advances CPT simulations by incorporating the fabric anisotropy variable A into the SANISAND-F model and utilizing the Material Point Method (MPM), with a specific focus on the role of evolving fabric within an anisotropic critical state framework. The objective is to deepen the understanding of how the evolving fabric of soils influences macroscopic site characterization outcomes. Through carefully controlled initial conditions, including void ratio and confining pressure, the study aims to demonstrate the impact of fabric anisotropy on CPT resistance measurements. Assessment of the evolution of material state based on the key constitutive ingredient of the model allows for explaining the reason behind the respective values of cone tip resistance observed from the CPT simulations, considering fabric anisotropy and the anisotropic critical state framework. This approach enhances the modeling of this site characterization method, providing a more comprehensive framework for interpreting soil mechanical behavior and enhancing predictive modeling capabilities
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== Full Paper ==
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<pdf>Media:Draft_Sanchez Pinedo_868240187293.pdf</pdf>

Revision as of 11:27, 10 June 2024

Abstract

In geotechnical site characterization, Cone Penetration Testing (CPT) is a fundamental method for evaluating subsurface conditions of granular materials such as sands, silts, and non-plastic tailings. This study advances CPT simulations by incorporating the fabric anisotropy variable A into the SANISAND-F model and utilizing the Material Point Method (MPM), with a specific focus on the role of evolving fabric within an anisotropic critical state framework. The objective is to deepen the understanding of how the evolving fabric of soils influences macroscopic site characterization outcomes. Through carefully controlled initial conditions, including void ratio and confining pressure, the study aims to demonstrate the impact of fabric anisotropy on CPT resistance measurements. Assessment of the evolution of material state based on the key constitutive ingredient of the model allows for explaining the reason behind the respective values of cone tip resistance observed from the CPT simulations, considering fabric anisotropy and the anisotropic critical state framework. This approach enhances the modeling of this site characterization method, providing a more comprehensive framework for interpreting soil mechanical behavior and enhancing predictive modeling capabilities

Full Paper

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Published on 10/06/24
Submitted on 10/06/24

Volume Numerical modelling of in-situ tests, 2024
DOI: 10.23967/isc.2024.293
Licence: CC BY-NC-SA license

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