Evaluating the uncertainty in cyclic resistance of sand using a practical cDSS test database

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4. Evaluating the uncertainty in cyclic resistance of sand using a practical cDSS test database

With the rising demand for offshore wind energy, there is a noticeable trend towards deploying wind turbines with larger blade sizes, positioned farther from coastlines, and facing more complex environmental conditions. A comprehensive understanding of the mechanical properties of marine sediments can facilitate addressing this development trend. The cyclic resistance capacity of soil is typically determined by laboratory tests such as cyclic triaxial (TX) tests and cyclic direct simple shear (DSS) tests. However, due to the inherent variability of soil properties and measurement uncertainties, there is uncertainty on the results of laboratory tests, which has not been thoroughly studied. In this study, results from publicly available data from offshore wind farm projects in Netherlands, are collected and normalized to establish a database. The database comprises various density sediments and different loading conditions, including the basic parameters (e.g., relative density, consolidation stress, over-consolidation ratio) and cyclic properties of the soils (e.g., shear cycle number at specified strain levels). Statistical analysis methods are used to analyse constant volume cyclic DSS testing on marine sands. The database provided three fitting models to obtain cyclic shear stress-number of shear cycles degradation curves, which present reference for further design.  In most cases, an increase in average shear stress can enhance the cyclic resistance of the sample, leading to uncertainty. The influence of pre-shear on cyclic resistance varies. Additionally, the correlations between the failure cyclic shear stress ratio at the 10th cycle and the void ratio, effective vertical stress, and over-consolidation are discussed.

5th International Symposium on Frontiers in Offshore Geotechnics (ISFOG2025)

5 - Data Analytics and Machine Learning