Empirical Truncation Design of Deepwater Mooring System Using Supervised Learning Method

Abstract

Truncated mooring system is the foundation of hybrid model test of deepwater floating platform with spread mooring system. This paper presents supervised learning method using linear regression model as the learner to generate empirical formulas which can determine the properties of truncated mooring system given the properties of full-depth mooring system. Thousands of completed truncation tasks are used to train the leaner, and then empirical formulas determining the length, axial stiffness and wet weight of the truncated system are established. The formulas are tested using a set of new truncation tasks. The results indicate that the truncated mooring system can be properly predicted by simply using the formulas.