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Due to the complex geological, meteorological and hydrological conditions, along with the limited availability of field measurements, multiple uncertainties are related to the performance analysis of the offshore wind turbine. The main sources of uncertainty are the geological model and the environmental loads. However, the geological model uncertainty and loads uncertainty were often characterized separately in previous studies. To this end, this article proposes a novel framework for the coupled characterization of the geological model and loads uncertainties. With this new framework, the geological model is generated using a conditional random field method, in which the stratigraphic and geo-properties uncertainties are characterized simultaneously. Next, environmental parameters are considered as random variables, and the cross correlation between different environmental parameters is captured with the Pearson correlation coefficient. Then, multiple realizations of the geological model and these of environmental parameters are sampled with Monte Carlo simulation. With these generated realizations as inputs, the monopile foundation performance could then be analyzed probabilistically considering uncertainties of the geological model and loads. To demonstrate the effectiveness of the new framework, a case study of an offshore site in Taiwan is conducted. Further, a comparative study is performed to quantitatively reveal the influence of different uncertainty sources on site characterization and monopile behaviour assessment, through which the superiority of the presented framework is demonstrated. [ABSTRACT FROM AUTHOR]

Site characterization, which aims to characterize the subsurface stratigraphic configuration and the associated geo-properties, has long been a significant challenge in geological and geotechnical practice. Due to the complexity and inherent spatial variability of the geological bodies and the limited availability of borehole data, uncertainty is unavoidable in the characterized subsurface stratigraphic configuration and the associated geo-properties. In previous studies, the stratigraphic uncertainty and the geo-properties uncertainty were characterized separately. This paper proposes a conditional random field approach for a coupled characterization of stratigraphic and geo-properties uncertainties. The spatial correlation of the stratum existence between different subsurface elements and the spatial correlation of geo-properties are characterized by two autocorrelation functions, determined with the maximum likelihood principle. With the knowledge of the spatial correlation of the stratum existence, the stratigraphic configuration can be sampled using a modified random field approach. Then, the spatial correlation of the geo-properties is updated based on the sampled stratigraphic configuration. With the updated spatial correlation of the geo-properties, the spatial distribution of the geo-properties can readily be simulated with the conditional random field theory. The effectiveness of the proposed approach is demonstrated through a case study of probabilistic site characterization of an offshore wind farm site in Taiwan. To extend the applicability of the proposed approach, a probabilistic evaluation of liquefaction potential at this site under a given seismic shaking level is performed. • A probabilistic method for characterizing the geological model is proposed. • Characterizations of stratigraphic and geo-properties uncertainties are coupled. • Stratigraphic configuration is simulated with modified random field approach. • Spatial variability of geo-properties is simulated with conditional random field. • The spatial correlation structure is estimated with maximum likelihood principle. [ABSTRACT FROM AUTHOR]