Similarity quantification of soil spatial variability between two cross-sections using auto-correlation functions

Hu, Yue, Wang, Yu, Phoon, Kok-Kwang and Beer, Michael ORCID: 0000-0002-0611-0345 (2024) Similarity quantification of soil spatial variability between two cross-sections using auto-correlation functions. Engineering Geology, 331. p. 107445.

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Official URL: http://dx.doi.org/10.1016/j.enggeo.2024.107445

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.

Item Type:	Article
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	05 Mar 2024 08:31
Last Modified:	05 Mar 2024 08:32
DOI:	10.1016/j.enggeo.2024.107445
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3179126