Data mining in sloshing experiment database and application of neural network for extreme load prediction
Yangjun Ahn, Yonghwan Kim
Abstract
Database of the sloshing model test has been mined. More than 540 terabytes experimental data have been accumulated for various cargo holds, vessels, environmental conditions, operational conditions, and experimental conditions. The database was organized, cleaned, and analyzed for the floating units larger than standard size LNG carriers or LNG fueled vessels. The selected target data was used for the machine learning to predict the model test results from the test conditions. An artificial neural network has been developed. Many different types of parameters were scaled and transformed as the input attributes followed by the optimization of the hyperparameters and the architecture. The network predicted the test results that were not used in the training process. The prediction results were validated according to the changes in the environmental conditions, operational conditions, and model dimensions. The accuracy of the network was acceptable to be applicable to the designing perspective.