In the global energy transition process, offshore wind is rapidly emerging as a crucial energy source. This shift has led to an increasing demand for submarine cables to connect offshore wind farms to onshore substations, with cable installation costs representing 9% of the total installation expenses. Cables are buried in the seabed, primarily through cable ploughing, adopting a special plough to create narrow trenches up to 3 m deep in the seabed. Accurate prediction of the vessel tow force is essential for efficient cable installation design, with the tow force being influenced by soil type, burial depth, and target velocity. Currently, available analytical approaches for sands rely on empirical correction factors, leading to inaccuracy in application across different scenarios. Advanced numerical methods could be in principle adopted to study the complex hydromechanical system response, but at the cost of significant computational resources and with the limitations related to field soil characterization. To address these limitations, artificial intelligence (AI)–based modeling has emerged as a promising alternative in many engineering fields, where wide data sets are available, as in cable ploughing applications due to the abundance of operational data. This study provides a comparison of the predictive capabilities of three literature analytical models against new field data from three different cable ploughing projects in sands, highlighting their limitations and proving the potential of the support vector machine (SVM) regression models in enhancing the prediction of the tow force. Remarkably, the developed SVM model only requires input parameters that are available from standard offshore geotechnical investigations, like cone penetration test results and soil granulometry. The study is based on 113 km of cable ploughing data, emphasizing the applicability of adaptable predictive models in the field of offshore cable installation.
P. Marveggio, A. Romero, Davide Gritti et al.· Journal of Geotechnical and...· 0 citations
The occurrence of flotation phenomena during trench backfilling remains a well-recognized threat to the successful installation of offshore pipelines, with direct implications for both safety and cost. This study developed and assessed a simplified model based on Kynch’s batch sedimentation theory to predict the buoyant response of pipelines during sand–water backfilling. The formulation couples the temporal evolution of solid concentration with the dynamic equilibrium of the pipe, accounting for both buoyancy and Bingham-type drag forces. The model was assessed through comparison with a set of small-scale laboratory test results, and good agreement was found in terms of concentration evolution, uplift onset time, and pipe displacement evolution. The results of numerical parametric studies provide insight into the influence of pipe specific gravity, revealing its impact on the evolving balance between driving and resisting forces—from the onset of flotation to eventual resettlement. The results confirm that, despite the simplifying assumptions, the formulation captures the fundamental physics of pipe flotation and resettlement. The model offers a sound theoretical framework that can support future design-oriented studies aimed at identifying safe combinations of pipe density, trench geometry, and backfilling conditions in offshore installations.
F. Cecinato, G. Della Vecchia, P. Eikhout et al.· Journal of Geotechnical and...· 0 citations
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