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Abstract
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.
Original language | English |
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Pages (from-to) | 1221-1231 |
Journal | Journal of Rock Mechanics and Geotechnical Engineering |
Volume | 14 |
Issue number | 4 |
Online published | 18 Mar 2022 |
DOIs | |
Publication status | Published - Aug 2022 |
Funding
The work described in this paper was supported by grants from the Research Grant Council of Hong Kong Special Administrative Region, China (Project Nos. CityU 11202121 and CityU 11213119). The financial support is gratefully acknowledged.
Research Keywords
- Compressive sampling
- Cone penetration test (CPT)
- Information entropy
- Liquefaction potential
- Site characterization
Publisher's Copyright Statement
- This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/
Fingerprint
Dive into the research topics of 'Adaptive sampling strategy for characterizing spatial distribution of soil liquefaction potential using cone penetration test'. Together they form a unique fingerprint.Projects
- 2 Finished
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GRF: Multiscale Machine Learning of Subsurface Stratigraphy from Limited Site-specific Measurements and Prior Geological Knowledge using Iterative Convolutional Neural Networks (CNN)
WANG, Y. (Principal Investigator / Project Coordinator)
1/01/22 → 2/10/24
Project: Research
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GRF: Development of Machine Learning Methods for Planning of Geotechnical Site Investigation and Analytics of Site Investigation Data
WANG, Y. (Principal Investigator / Project Coordinator)
1/01/20 → 9/08/23
Project: Research