Abstract
National Natural Science Foundation of China (11702199), Qian Shao. National Natural Science Foundation of China (URL: nsfc.gov.cn).
Highlights
Seepage in porous media is a common phenomenon in civil engineering that provides both advantages and disadvantages
The influence of solid deformation on the fluid flow is taken into account by including the porosity change in governing equations. This fluid-solid interaction during seepage consolidation process can be simulated accurately by solving these governing equations using appropriate numerical methods in the premise that all the model parameters are predetermined by experiments, which is a difficult task in real geotechnical applications as the material properties are typically heterogeneous and anisotropic
Where, D is the stiffness tensor defined by effective Young’s modulus and Poisson’s ratio of the porous media, ε = ∇u + ∇T u /2 is the strain tensor related to the displacement tensor, and αB is Biot’s poroelasticity constant defined by αB = (KS − KT ) /KS, with KT and KS representing the bulk modulus of soil sample and solid grains, respectively [19,40]
Summary
Seepage in porous media is a common phenomenon in civil engineering that provides both advantages and disadvantages. The influence of solid deformation on the fluid flow is taken into account by including the porosity change in governing equations This fluid-solid interaction during seepage consolidation process can be simulated accurately by solving these governing equations using appropriate numerical methods in the premise that all the model parameters are predetermined by experiments, which is a difficult task in real geotechnical applications as the material properties are typically heterogeneous and anisotropic. We use a new algorithm proposed by Shao et al [32] to perform SA and assess uncertainty on the seepage consolidation model in fractured porous media This algorithm constructs sparse polynomial chaos expansion (SPCE), representing the input-output relation based on the Bayesian model averaging.
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