Risk-informed adaptive sampling strategy for liquefaction severity mapping

ABSTRACT

In engineering practice, liquefaction severity map is usually developed from liquefaction potential index (LPI) which is estimated using in-situ tests, such as cone penetration tests (CPTs). To efficiently perform in-situ tests for obtaining a reliable liquefaction severity map, it is advantageous to use adaptive sampling strategy, which uses results from initial measurements to sequentially decide CPT number and locations in a later stage. For example, the optimal number and locations of subsequent CPTs may be determined for maximising the reduction of overall uncertainty in the interpolated LPI data over the map obtained from a preliminary stage. However, uncertainty-based adaptive sampling might not provide optimal results for liquefaction severity mapping because the threshold of liquefaction severity classification is not considered. To properly evaluate the reliability of interpreted liquefaction severity map, a reliability index, $\text{β}$, is proposed in this study and further used to determine the optimal number and locations of in-situ tests. The proposed risk-informed adaptive sampling strategy is illustrated and compared with the uncertainty-based strategy. The example shows that the proposed method is more efficient than the uncertainty-based strategy.

KEYWORDS:

• Liquefaction severity map
• adaptive sampling
• reliability index
• uncertainty-based sampling
• risk-informed sampling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work described in this paper was supported by a grant from the Research Grant Council of Hong Kong Special Administrative Region (Project No: CityU 11203322), a grant from The Science and Technology Development Fund, Macau Special Administrative Region (File/Project No: SKL-IOTSC(UM)-2021–2023), and a grant from Shenzhen Science and Technology Innovation Commission (Shenzhen-Hong Kong-Macau Science and Technology Project (Category C) No: SGDX20210823104002020), China. The financial support is gratefully acknowledged.