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Published December 10, 2017 | public
Journal Article

Predicting the initiation of static liquefaction of cross-anisotropic sands under multiaxial stress conditions


Experimental evidence has shown that the liquefaction instability of sands can be affected by its material density, stress state, and inherent anisotropy. In order to predict the initiation of the static liquefaction of inherent cross-anisotropic sands under multidimensional stress conditions, a rational constitutive model is needed. An elastoplasticity model able to capture the influences of intermediate principal stress ratio (b = (σ_2 – σ_3)/(σ_1 – σ_3)) and loading direction on stress–strain relationships and volumetric properties was proposed. The yield function was formulated to be controlled by Lode angle, loading direction, and material state; the stress–dilatancy was a material state-dependent function. After using the existing drained hollow cylinder tests to validate the proposed model, this model was used to simulate the existing undrained hollow cylinder tests. During this simulation, the second-order work criterion was used to determine the initiation of static liquefaction. The results showed that an increase in both the intermediate principal stress ratio and the loading angle induces a decrease in the second-order work. Static liquefaction is initiated more easily at a stress state with a large intermediate principal stress ratio and a large loading angle, and the mobilized friction angle at the instability points decreases with the intermediate principal stress ratio and the loading angle.

Additional Information

© 2017 John Wiley & Sons, Ltd. Issue online: 15 November 2017; Version of record online: 11 May 2017; Manuscript Accepted: 2 April 2017; Manuscript Revised: 23 March 2017; Manuscript Received: 4 April 2016. Funded by: National Science Foundation of China. Grant Number: 11372228; National Key Research and Development Program. Grant Number: 2016YFC0800202; National Science Foundation of China. Grant Number: 41672270; National Natural Science Foundation of China. Grant Number: U1433123.

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October 17, 2023