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

verfasst von: 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.
Organisationseinheit(en): Institut für Risiko und Zuverlässigkeit
Externe Organisation(en): City University of Hong Kong
Singapore University of Technology and Design
The University of Liverpool
Tongji University
Typ: Artikel
Journal: Engineering geology
Band: 331
Anzahl der Seiten: 21
ISSN: 0013-7952
Publikationsdatum: 03.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Geotechnik und Ingenieurgeologie, Geologie
Elektronische Version(en): https://doi.org/10.1016/j.enggeo.2024.107445 (Zugang: Geschlossen)

BibTeX

@article{e33066b7c5eb4091b20fc6ba1d387082,
title = "Similarity quantification of soil spatial variability between two cross-sections using auto-correlation functions",
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.",
keywords = "Auto-correlation, Bayesian compressive sensing, Geotechnical site investigation, Site similarity",
author = "Yue Hu and Yu Wang and Phoon, {Kok Kwang} and Michael Beer",
note = "Funding Information: 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 ) 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. The first author would also thank the support of the Alexander von Humboldt Foundation, Germany. ",
year = "2024",
month = mar,
doi = "10.1016/j.enggeo.2024.107445",
language = "English",
volume = "331",
journal = "Engineering geology",
issn = "0013-7952",
publisher = "Elsevier",
}