2022 Volume 78 Issue 2 Pages I_1-I_6
Crew transfer vessels (CTVs) are the main personnel transport vehicle in offshore wind farm businesses. Accurate evaluation of the accessibility to the offshore wind turbines (OWTs) by CTVs is very important to ensure the safety of personnel and to improve work efficiency. This study developed an artificial neural network (NN) model to evaluate the accessibility based on real experimental data of CTVs. Sensitivity analysis was carried out to evaluate the effect of wave and wind parameters on the determination of the accessibility of CTVs. It is found that NN models show great potential for predicting the accessibility of CTVs. Wave condition plays a dominant role in the success of transfer by using CTVs. Using wind conditions as additional input parameters for NN models can slightly increase the classification accuracy. The classification error is believed due to the insufficiency of the objectivity of the dataset.
