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Volume 40 Issue 2
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CAO Zi-jun, ZHENG Shuo, LI Dian-qing, AU Sui-kiu. Probabilistic characterization of underground stratigraphy and its uncertainty based on cone penetration test[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(2): 336-345. DOI: 10.11779/CJGE201802015
Citation: CAO Zi-jun, ZHENG Shuo, LI Dian-qing, AU Sui-kiu. Probabilistic characterization of underground stratigraphy and its uncertainty based on cone penetration test[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(2): 336-345. DOI: 10.11779/CJGE201802015
shu

Probabilistic characterization of underground stratigraphy and its uncertainty based on cone penetration test

  • 1.State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
    2.Institute for Risk and Uncertainty, University of Liverpool, Liverpool L69 3GH, United Kingdom

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  • Received Date: November 04, 2016
  • Published Date: February 24, 2018
    Graphical Abstract
    • Abstract
  • A Bayesian framework is developed to probabilistically identify the underground stratigraphy based on Ic data. The proposed Bayesian framework identifies the most probable soil layer boundaries with the consideration of spatial variability of Ic and quantifies the uncertainties in the underground stratigraphy, which provides valuable information for making future site investigation plans and geotechnical designs. A subset simulation-based Bayesian updating algorithm (CBUS) is used to generate posterior samples of soil layer thicknesses and to calculate the model evidence for determining the most probable number of soil layers and the most probable soil layer boundaries, and the standard deviations of boundaries are calculated to quantify the uncertainty in soil layer boundaries. Finally, the proposed approach is illustrated and verified using the real Ic data obtained from a deep excavation site at Yili station of Shanghai No. 10 subway line and simulated Ic data from a virtual site. The results show that the underground stratigraphy identified by the proposed approach is based on the statistical similarity of Ic data. With the increase of statistical difference in Ic data within two adjacent soil layers, the standard deviation of the soil layer boundary between them decreases, and the soil layer boundary identified by the proposed approach is more reliable, and vice versa.
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    • References (40)
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