Details
| Original language | English |
|---|---|
| Article number | 107445 |
| Number of pages | 21 |
| Journal | Engineering geology |
| Volume | 331 |
| Early online date | 13 Feb 2024 |
| Publication status | Published - Mar 2024 |
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
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Geotechnical Engineering and Engineering Geology
- Earth and Planetary Sciences(all)
- Geology
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In: Engineering geology, Vol. 331, 107445, 03.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Similarity quantification of soil spatial variability between two cross-sections using auto-correlation functions
AU - Hu, Yue
AU - Wang, Yu
AU - Phoon, Kok Kwang
AU - Beer, Michael
N1 - 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.
PY - 2024/3
Y1 - 2024/3
N2 - 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.
AB - 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.
KW - Auto-correlation
KW - Bayesian compressive sensing
KW - Geotechnical site investigation
KW - Site similarity
UR - http://www.scopus.com/inward/record.url?scp=85185834404&partnerID=8YFLogxK
U2 - 10.1016/j.enggeo.2024.107445
DO - 10.1016/j.enggeo.2024.107445
M3 - Article
AN - SCOPUS:85185834404
VL - 331
JO - Engineering geology
JF - Engineering geology
SN - 0013-7952
M1 - 107445
ER -