کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
507628 | 865136 | 2013 | 14 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Using state parameter to improve numerical prediction of a generalized plasticity constitutive model Using state parameter to improve numerical prediction of a generalized plasticity constitutive model](/preview/png/507628.png)
Dilatancy, as a principal feature of sand behavior, is significantly affected by state of the sand. State of the soil is determined by density and mean principal effective stress. Constitutive models which fail to consider this state dependency of sand samples are unable to predict both dilative and contractive behavior of the soil using a unique set of model constants. In fact, state independent constitutive models have to redefine their model constants upon any changes in states of the soil. Several empirical correlations between internal parameters of constitutive models and several state variables have been proposed in literature. This study aims to investigate how implementing state parameter in a constitutive model can improve numerical predictions. Herein, some of the empirical correlations in literature are adopted in a generalized plasticity constitutive model. Afterwards some single element and multi element tests are simulated applying both state independent and modified state dependent constitutive models. Outcomes of the numerical simulations are used to assess the proposed modification. All in all, the results show that numerical analysis using state dependent constitutive model results in more accurate numerical predictions. Since, the proposed modifications make the constitutive model compatible with critical state theory and readjustment of constant parameters are no longer required. Simplicity is another advantage of the modified presented state dependent constitutive model inasmuch as no new material constant needs to be adopted.
► Implementing state parameter in an independent constitutive model.
► How state dependent numerical analysis can improve predictions.
► Investigating the effect of state parameter in numerical predictions.
Journal: Computers & Geosciences - Volume 51, February 2013, Pages 255–268