Similarity quantification of soil spatial variability between two cross-sections using auto-correlation functions
- authored by
- Yue Hu, Yu Wang, Kok Kwang Phoon, Michael Beer
- Abstract
In geotechnical engineering, an appreciation of local geological conditions from similar sites is beneficial and can support informed decision-making during site characterization. This practice is known as “site recognition”, which necessitates a rational quantification of site similarity. This paper proposes a data-driven method to quantify the similarity between two cross-sections based on the spatial variability of one soil property from a spectral perspective. Bayesian compressive sensing (BCS) is first used to obtain the discrete cosine transform (DCT) spectrum for a cross-section. Then DCT-based auto-correlation function (ACF) is calculated based on the obtained DCT spectrum using a set of newly derived ACF calculation equations. The cross-sectional similarity is subsequently reformulated as the cosine similarity of DCT-based ACFs between cross-sections. In contrast to the existing methods, the proposed method explicitly takes soil property spatial variability into account in an innovative way. The challenges of sparse investigation data, non-stationary and anisotropic spatial variability, and inconsistent spatial dimensions of different cross-sections are tackled effectively. Both numerical examples and real data examples from New Zealand are provided for illustration. Results show that the proposed method can rationally quantify cross-sectional similarity and associated statistical uncertainty from sparse investigation data. The proposed method advances data-driven site characterization, a core application area in data-centric geotechnics.
- Organisation(s)
-
Institute for Risk and Reliability
- External Organisation(s)
-
City University of Hong Kong
Singapore University of Technology and Design
University of Liverpool
Tongji University
- Type
- Article
- Journal
- Engineering geology
- Volume
- 331
- No. of pages
- 21
- ISSN
- 0013-7952
- Publication date
- 03.2024
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology, Geology
- Electronic version(s)
-
https://doi.org/10.1016/j.enggeo.2024.107445 (Access:
Closed)
