Suction piles, also known as suction caissons or suction anchors, have been used extensively in the offshore industry since the 1980s. Existing design methods, such as the DNV-GL method, were primarily developed from installations in dense sands or clays. However, with the continued growth in offshore wind energy, suction piles are being installed more and more in intermediate soils, such as silt, sand and clay mixtures. However, problems can arise when using current design methods for forecasting installation response in these soils. Furthermore, it’s also challenging to classify the soil based on CPT measurements, as well as selecting the appropriate design factors for the suction pile’s shaft and base resistance. To evaluate how design methods perform in intermediate soils, a database has been compiled of installation records across 126 piles at three different sites. This paper focuses on the suction installation phase, comparing the performance of different design methods and soil classification methods in predicting the required suction pressures. The performance of three different design methods have been compared, in addition to a sensitivity analysis on the design factors—indicating the likely range of these factors for intermediate soils.

This study presents a probabilistic analyses of suction bucket installation in cohesionless soils. The spatial variability of soil properties is quantified using a representative survey dataset from practice. Vertical random field modelling is used to model cone resistance variability and probability density functions are fitted using drained parameter estimates. Conventional installation analysis methods are adapted for layered soil deposits. The effect of varying permeability is included through the incorporation of a finite element seepage model. Parametric uncertainties are considered through a Monte Carlo analysis and the results are interrogated through a variety of feasibility and sensitivity studies. Performing such an analysis allows a designer to gain insights into governing failure mechanisms and objectively quantify the impact of uncertainty regarding parameter estimates.