PREDICTION AND ANALYSIS OF FLEXURAL STRENGTH OF SEA ICE BASED ON RECURRENT NEURAL NETWORK

As a mechanical property of sea ice, the flexural strength is an important parameter for determining the ice load of inclined engineering structures, such as conical offshore platforms, ships, ports and wharves. In the paper, the three-point bending test data of Bohai Sea ice are summarized from the year of 2009 to 2010. The effects of temperature, salinity, loading rate and density on the flexural strength of sea ice are analyzed. Then, the RNN (recurrent neural network) model for predicting the flexural strength of sea ice is established based on the test data, and the model is optimized by the random gradient descent method and Adam optimization algorithm. The parameters such as temperature, salinity, loading rate and density of sea ice are taken as the input and the flexural strength is taken as the output. The data were sorted randomly and normalized, and the accuracy of the model is evaluated by using evaluation indexes. The predicted results are compared with the empirical formula from experiments. The results show that the prediction accuracy using the RNN model is significantly better than that of the empirical formula. This model can effectively solve the complex nonlinear relationship between the flexural strength of sea ice and its influencing factors, and provide a reference for the anti-ice structure design in offshore engineering.