Uncertainty Analysis of Seepage-Induced Consolidation in a Fractured Porous Medium

Lingai Guo, Marwan Fahs, Hussein Hoteit, Rui Gao, Qian Shao

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Numerical modeling of seepage-induced consolidation process usually encounters significant uncertainty in the properties of geotechnical materials. Assessing the effect of uncertain parameters on the performance variability of the seepage consolidation model is of critical importance to the simulation and tests of this process. To this end, the uncertainty and sensitivity analyses are performed on a seepage consolidation model in a fractured porous medium using the Bayesian sparse polynomial chaos expansion (SPCE) method. Five uncertain parameters including Young’s modulus, Poisson’s ratio, and the permeability of the porous matrix, the permeability within the fracture, and Biot’s constant are studied. Bayesian SPCE models for displacement, flow velocity magnitude, and fluid pressure at several reference points are constructed to represent the input-output relationship of the numerical model. Based on these SPCE models, the total and first-order Sobol’ indices are computed to quantify the contribution of each uncertain input parameter to the uncertainty of model responses. The results show that at different locations of the porous domain, the uncertain parameters show different effects on the output quantities. At the beginning of the seepage consolidation process, the hydraulic parameters make major contributions to the uncertainty of the model responses. As the process progresses, the effect of hydraulic parameters decreases and is gradually surpassed by the mechanical parameters. This work demonstrates the feasibility to apply Bayesian SPCE approach to the uncertainty and sensitivity analyses of seepage-induced consolidation problems and provides guidelines to the numerical modelling and experimental testing of such problems.
Original languageEnglish (US)
Pages (from-to)279-297
Number of pages19
Issue number1
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-09-07
Acknowledgements: National Natural Science Foundation of China (11702199), Qian Shao. National Natural Science Foundation of China (URL: nsfc.gov.cn).

ASJC Scopus subject areas

  • Modeling and Simulation
  • Software
  • Computer Science Applications


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