Spatial variability characterization of soil properties in offshore wind farms based on Bayesian theory and conditional co-simulation method

XU Jiabao; ZHANG Zechao; ZHANG Lulu; CAO Zijun; WANG Yu; ZHANG Yifan; ZHANG De; CHEN Yangming

doi:10.11779/CJGE20221585

Volume 46 Issue 8

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Chinese Journal of Geotechnical Engineering > 2024 > 46(8): 1644-1654. > DOI: 10.11779/CJGE20221585

XU Jiabao, ZHANG Zechao, ZHANG Lulu, CAO Zijun, WANG Yu, ZHANG Yifan, ZHANG De, CHEN Yangming. Spatial variability characterization of soil properties in offshore wind farms based on Bayesian theory and conditional co-simulation method[J]. Chinese Journal of Geotechnical Engineering, 2024, 46(8): 1644-1654. DOI: 10.11779/CJGE20221585

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Spatial variability characterization of soil properties in offshore wind farms based on Bayesian theory and conditional co-simulation method

XU Jiabao^{1, 2,},
ZHANG Zechao³,
ZHANG Lulu^{1, 4, 5, ,},
CAO Zijun⁶,
WANG Yu²,
ZHANG Yifan¹,
ZHANG De¹,
CHEN Yangming¹

1.
State Key Laboratory of Ocean Engineering, Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2.
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, China
3.
Institute of Science and Technology, China Three Gorges Corporation, Beijing 100038, China
4.
Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration (CISSE), Shanghai 200240, China
5.
Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai 200240, China
6.
MOE Key Laboratory of High-speed Railway Engineering, Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611756, China

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Received Date: December 28, 2022
Available Online: March 24, 2024

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Abstract

Abstract

The spatial variability characterization of soil properties in offshore wind farms is essential for offshore engineering. The multi-source data fusion can reduce the uncertainty of characterization. However, the existing methods cannot simulate geotechnical properties based on the non-co-located multi-source data, and do not consider the effects of statistical uncertainty. To overcome these challenges, a conditional co-simulation method based on the Bayesian theory is proposed. The Bayesian theory is first used to estimate the cross-variogram model based on the non-co-located multi-source data. Then, the conditional co-simulation is used to generate realizations of spatially varied soil properties, which can characterize the spatial variability with consideration of statistical uncertainty. The proposed method is applied to an offshore wind farm to establish the spatial variability model for the unconfined compression strength (q_u) by integrating data on q_u and standard penetration test (SPT) N value. The results show that the proposed method can characterize the spatial variability of q_u from the non-co-located data on the values of q_u and N, and statistical uncertainty is properly taken into account. In addition, the uncertainties of the variogram models and the conditional co-simulation results can be reduced when the prior distribution with more information and/or SPT data is used.
- offshore wind farm,
- spatial variability,
- conditional co-simulation,
- Bayesian theory,
- multi-source data fusion

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References (27)

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