With the rapid development of offshore wind energy in Europe, a large number of piled structures are being installed. In areas with sandy seabed conditions, erosion of sediment by the actions of wave and current can negatively influence foundation capacity. An accurate prediction model of scour around the piles is therefore required. Well-accepted scour prediction methods exist; both for the equilibrium scour depth and the time scale of scour [1] around single piles. These standard formulas have been combined with metocean data and a hindcasting model to calculate the expected scour depth around piles of wind turbine tripod foundations. Other causes of scour, such as pile-pile interaction, effect of proximity of structural members to the seabed, and seabed mobility were also assessed in order to determine the amount of global scour to be considered. The scour predictions were compared to measurements taken at an offshore wind turbine foundation at Park Alpha Ventus (PAV) in the German North Sea [2]. The data showed very good agreement with the measured scour around the piles. Both the equilibrium scour depth and time scale of scour were well predicted using the hindcasting model. The measured scour below the central column of the tripod structure exceeded expectations; this is believed to be due to a pumping effect during storm episodes. Finally, the effect of scour on the vertical effective stress around the tripod piles was assessed with a finite element model. Local scour had an important effect while scour below the centre of the structure had a much more limited effect. Considering the combined effects of multiple pile interaction, scour below the central column, and making an allowance for seabed mobility, an equivalent global scour depth for pile capacity calculations was established.
The application of artificial intelligence (AI) and big data in geohazard investigations has gained popularity due to the development of machine learning algorithms and data collection methods. Previous studies have compared and applied various machine learning‐based methods, such as conventional machine learning, deep learning, and transfer learning in different areas. This special issue provides state‐of‐the‐art information on the use of AI in geotechnical research, particularly in the Three Gorges Reservoir (TGR) area and adjoining regions. The aim of this volume is to serve as a reference for future researchers interested in exploring the potential of AI in geohazard investigations. It is hoped that this special issue will contribute to the development of guidelines for enhancing the application of AI and big data in geotechnical research, thereby improving our understanding of geological terrains and their associated hazards.
Offshore wind turbines (OWTs) are sensitive, dynamic structures that require accurate estimation of the natural frequency of the support structure. This requires an integrated analysis of the wind turbine structure, including a realistic representation of the foundation response. This paper studies how the natural frequency analysis is influenced by the soil-monopile interaction model following the PISA guidance. A case study of an instrumented OWT at a Belgian offshore wind farm is presented. Comparing results between the calculated and monitored natural frequencies clearly demonstrates that a realistic representation of the foundation stiffness is crucial for accurate calculation of the OWT dynamics. The integrated analysis of the OWT based on the calibrated foundation model yields very accurate results.
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