Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Characterizing the spatial variability of marine soil properties with site-specific sparse data using a Bayesian data fusion approach
Sparse site-specific test data complicates soil spatial variability characterization, resulting in substantial statistical uncertainty in model parameters. Rare studies explicitly address this uncertainty, a more pronounced issue in offshore wind engineering due to large and multi-source yet sparse and non-co-located data. This study proposes a Bayesian conditional co-simulation (BCCS) method for spatial variability characterization of marine soils in offshore wind farms. Utilizing primary (e.g., internal friction angle, ϕ) and secondary (e.g., standard penetration test, SPT N values) variable measurements, the BCCS method employs a Bayesian framework to infer variogram model parameters and to quantify statistical uncertainty. Notably, the statistical uncertainty is considered in subsequent conditional co-simulation of the primary variable. The proposed approach is applied to characterizing the spatial variability of ϕ based on measurements of ϕ and SPT N in a sand layer in an offshore wind farm. The proposed methodology effectively characterizes marine soil spatial variability using sparse non-co-located primary and secondary datasets. Neglecting statistical uncertainty in variogram model parameters underestimates the prediction uncertainty for the primary variable. This can be mitigated by incorporating an informative prior distribution, assimilating secondary data, and increasing primary data volume. Efficacy depends on existing knowledge and data quality.
Characterizing the spatial variability of marine soil properties with site-specific sparse data using a Bayesian data fusion approach
Sparse site-specific test data complicates soil spatial variability characterization, resulting in substantial statistical uncertainty in model parameters. Rare studies explicitly address this uncertainty, a more pronounced issue in offshore wind engineering due to large and multi-source yet sparse and non-co-located data. This study proposes a Bayesian conditional co-simulation (BCCS) method for spatial variability characterization of marine soils in offshore wind farms. Utilizing primary (e.g., internal friction angle, ϕ) and secondary (e.g., standard penetration test, SPT N values) variable measurements, the BCCS method employs a Bayesian framework to infer variogram model parameters and to quantify statistical uncertainty. Notably, the statistical uncertainty is considered in subsequent conditional co-simulation of the primary variable. The proposed approach is applied to characterizing the spatial variability of ϕ based on measurements of ϕ and SPT N in a sand layer in an offshore wind farm. The proposed methodology effectively characterizes marine soil spatial variability using sparse non-co-located primary and secondary datasets. Neglecting statistical uncertainty in variogram model parameters underestimates the prediction uncertainty for the primary variable. This can be mitigated by incorporating an informative prior distribution, assimilating secondary data, and increasing primary data volume. Efficacy depends on existing knowledge and data quality.
Characterizing the spatial variability of marine soil properties with site-specific sparse data using a Bayesian data fusion approach
Acta Geotech.
Zhang, Zechao (Autor:in) / Zhang, Yifan (Autor:in) / Zhang, Lulu (Autor:in) / Cao, Zijun (Autor:in) / Wang, Yu (Autor:in) / Yu, Yongtang (Autor:in) / Zheng, Jianguo (Autor:in)
Acta Geotechnica ; 20 ; 765-779
01.02.2025
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Conditional co-simulation , Data fusion , Offshore wind farm , Spatial variability , Statistical uncertainty Mathematical Sciences , Statistics , Information and Computing Sciences , Artificial Intelligence and Image Processing , Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
Characterizing Uncertain Site-Specific Trend Function by Sparse Bayesian Learning
| Online Contents | 2017
Characterizing Uncertain Site-Specific Trend Function by Sparse Bayesian Learning
| Online Contents | 2017