Poisson’s ratio of granular materials for Mohr-Coulomb elastoplastic model

ABSTRACTThe Mohr-Coulomb elastoplastic model is extensively employed in geotechnical engineering. Despite the well-known nonlinear behaviour of granular cohesionless materials under a wide range of normal stress or confining pressure, the Mohr-Coulomb elastoplastic model continues to be largely used in geotechnical engineering, due to its simplicity and large availability in numerous commercialised software. Its application requires the input of a key parameter, named Poisson’s ratio. It is however a big challenge as its value changes with axial strain and confining pressure. In this study, the optimal Poisson’s ratio of granular materials for the Mohr-Coulomb elastoplastic model is determined by reproducing the experimental results of stress-strain relationships through numerical modelling with the Mohr-Coulomb elastoplastic model. The results show that the Poisson’s ratio μ0.02, corresponding to the slope of the volumetric strain against axial strain curve at 2% of the peak deviatoric stress can be used in numerical modelling with the Mohr-Coulomb elastoplastic model as long as the granular material does not exhibit dilation behaviour. When the material exhibits dilation behaviour, the Poisson’s ratio μ0.36, corresponding to the slope of the volumetric strain against axial strain curve at 36% of the peak deviatoric stress, seems to be appropriate in numerical modelling with the Mohr-Coulomb elastoplastic model. In addition, the study also shows that the Mohr-Coulomb elastoplastic model can be used to simulate mechanical behaviour of granular material under small strain, but not appropriate under large strain conditions.KEYWORDS: Mohr-Coulomb modelPoisson’s ratiogranular materialscohesionless sandstriaxial compression testnumerical modellingstability and reliability of numerical modelling Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Young Scientist Project of the National Key Research and Development Program of China [No. 2021YFC2900600]; Beijing Nova Program [No. 20220484057]; China Scholarship Council [No. 202010300001]; Natural Sciences and Engineering Research Council of Canada [RGPIN-2018-06902 and ALLRP 566888-21].