Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas
Multi-source uncertainties yielded by randomness, spatial variability and cross-correlation of soil parameters severely affect the realization of random fields. However, current studies rarely account for these simultaneously, leading to inevitable bias in random field simulation and subsequent stru...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4234 |
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| author | Xinlong Zhou Yueyang Sun Henglin Xiao |
| author_facet | Xinlong Zhou Yueyang Sun Henglin Xiao |
| author_sort | Xinlong Zhou |
| collection | DOAJ |
| description | Multi-source uncertainties yielded by randomness, spatial variability and cross-correlation of soil parameters severely affect the realization of random fields. However, current studies rarely account for these simultaneously, leading to inevitable bias in random field simulation and subsequent structure analysis. In this paper, copula-based cross-correlated random fields for transversely anisotropic soil slope are proposed. Firstly, based on the traditional probabilistic method and random field theory, the effect of the cross-correlation of soil parameters on the random field is comprehensively analyzed. Then copulas, which mainly characterize the dependent structures of random variables, are further expanded to connect multivariate random fields. Four special algorithms associated with Gaussian, Frank, Plackett and No. 16 copulas are subsequently developed. At last, the performance and effectiveness of copula-based cross-correlated random fields are illustrated by means of assumed and engineering slope cases. The results show that the proposed method is amenable to characterizing spatial variability comprising multiple cross-correlated soil parameters of transversely anisotropic slope. Soil profiles can be represented with a relatively high accuracy. Moreover, the performance of copula-based CCRF is simultaneously governed by margins, cross-correlated coefficients and copulas. The proper selection of these crucial factors can considerably reduce multi-source uncertainties. Overall, the proposed method could provide a useful guideline for accurately modeling cross-correlation random fields of soil slope. |
| first_indexed | 2024-03-11T05:42:54Z |
| format | Article |
| id | doaj.art-6af02fcbabae464683bc042e67661601 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:42:54Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6af02fcbabae464683bc042e676616012023-11-17T16:17:26ZengMDPI AGApplied Sciences2076-34172023-03-01137423410.3390/app13074234Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by CopulasXinlong Zhou0Yueyang Sun1Henglin Xiao2Technology Research Center of Ecological Road Engineering, Hubei University of Technology, Wuhan 430068, ChinaTechnology Research Center of Ecological Road Engineering, Hubei University of Technology, Wuhan 430068, ChinaTechnology Research Center of Ecological Road Engineering, Hubei University of Technology, Wuhan 430068, ChinaMulti-source uncertainties yielded by randomness, spatial variability and cross-correlation of soil parameters severely affect the realization of random fields. However, current studies rarely account for these simultaneously, leading to inevitable bias in random field simulation and subsequent structure analysis. In this paper, copula-based cross-correlated random fields for transversely anisotropic soil slope are proposed. Firstly, based on the traditional probabilistic method and random field theory, the effect of the cross-correlation of soil parameters on the random field is comprehensively analyzed. Then copulas, which mainly characterize the dependent structures of random variables, are further expanded to connect multivariate random fields. Four special algorithms associated with Gaussian, Frank, Plackett and No. 16 copulas are subsequently developed. At last, the performance and effectiveness of copula-based cross-correlated random fields are illustrated by means of assumed and engineering slope cases. The results show that the proposed method is amenable to characterizing spatial variability comprising multiple cross-correlated soil parameters of transversely anisotropic slope. Soil profiles can be represented with a relatively high accuracy. Moreover, the performance of copula-based CCRF is simultaneously governed by margins, cross-correlated coefficients and copulas. The proper selection of these crucial factors can considerably reduce multi-source uncertainties. Overall, the proposed method could provide a useful guideline for accurately modeling cross-correlation random fields of soil slope.https://www.mdpi.com/2076-3417/13/7/4234spatial variabilitycross-correlated random fieldstransverse anisotropycopula |
| spellingShingle | Xinlong Zhou Yueyang Sun Henglin Xiao Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas Applied Sciences spatial variability cross-correlated random fields transverse anisotropy copula |
| title | Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas |
| title_full | Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas |
| title_fullStr | Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas |
| title_full_unstemmed | Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas |
| title_short | Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas |
| title_sort | simulation of cross correlated random fields for transversely anisotropic soil slope by copulas |
| topic | spatial variability cross-correlated random fields transverse anisotropy copula |
| url | https://www.mdpi.com/2076-3417/13/7/4234 |
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