Search

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]

Liquefaction potential analysis is a required task in the foundation design process of offshore wind turbine in Taiwan. The standard penetration test (SPT) is mostly used in the preliminary soil investigation of the pilot offshore wind farm in Chang-Bin, Taiwan. Due to the different experimental conditions and operating conditions, the N value (SPT-N) of SPT varies greatly. This study applies statistical methods in conjunction with the New Japan Road Association simplified-empirical method (NJRA method) to incorporate the uncertainty of SPT-N values into the offshore liquefaction potential assessment to quantify the risk of seabed liquefaction. The study statistics the field experimental geotechnical parameters SPT-N and determines the probability density function of the SPT-N distribution of each layer of soil in the offshore wind farm. In order to quantify the risk of seabed soil liquefaction potential, the Monte Carlo random sampling method is used, and by the NJRA method to carry out the seabed liquefaction potential in Changhua, Taiwan. After comparing, the results obtained by the current SPT-based soil liquefaction potential assessment by deterministic approach are conservative. In the ground investigation of offshore wind farm development, the method proposed in this paper can access the thickness of liquefiable soil layers under any given probability for optimizing offshore wind turbine foundation design. • We propose a new method to quantify the risk of the seabed soil liquefaction potential. • This method can assess the thickness of liquefiable soil layers under any given probability. • We present probability distribution of soil SPT-N obtained from offshore wind farms in Taiwan. • The effect of uncertainty of SPT-N on the soil liquefaction potential assessment is presented. [ABSTRACT FROM AUTHOR]