Journal
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
Volume 14, Issue 4, Pages 1221-1231Publisher
SCIENCE PRESS
DOI: 10.1016/j.jrmge.2022.01.011
Keywords
Liquefaction potential; Information entropy; Cone penetration test (CPT); Site characterization; Compressive sampling
Categories
Funding
- Research Grant Council of Hong Kong Special Administrative Region, China [CityU 11202121, CityU 11213119]
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Characterizing the spatial distribution of soil liquefaction potential is crucial for assessing liquefaction-related hazards. However, limitations in time, cost, and access to subsurface space often result in limited data points for measuring soil liquefaction potential. This study proposes a smart sampling strategy using information entropy and Bayesian compressive sampling to determine the minimum number and optimal locations of cone penetration tests (CPTs) for reliable liquefaction assessment.
Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefaction-related hazards (e.g. building damages caused by liquefaction-induced differential settlement). However, in engineering practice, soil liquefaction potential is usually measured at limited locations in a specific site using in situ tests, e.g. cone penetration tests (CPTs), due to the restrictions of time, cost and access to subsurface space. In these cases, liquefaction potential of soil at untested locations requires to be interpreted from limited measured data points using proper interpolation method, leading to remarkable statistical uncertainty in liquefaction assessment. This underlines an important question of how to optimize the locations of CPT soundings and determine the minimum number of CPTs for achieving a target reliability level of liquefaction assessment. To tackle this issue, this study proposes a smart sampling strategy for determining the minimum number of CPTs and their optimal locations in a self-adaptive and data-driven manner. The proposed sampling strategy leverages on information entropy and Bayesian compressive sampling (BCS). Both simulated and real CPT data are used to demonstrate the proposed method. Illustrative examples indicate that the proposed method can adaptively and sequentially select the required number and optimal locations of CPTs. (C) 2022 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V.
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