Full-scale identification of ice load on ship hull by least square support vector machine method

Abstract

For ensuring the structural safety of an icebreaker, the ice load is identified from the strains measured on the frames or plates of the hull along the waterline. The strain sensors are difficult to install, however, at the required locations on the hull because the watertight compartment is cramped. In this situation, the far-field load identification method can be applied to overcome such restriction on the installation location. To determine the nonlinear relationship between ice load and ice-induced strains, the least square support vector machine (LS_SVM) method is adopted. Among the advantages that this method affords for ice load identification are its capacity for small sample learning, global optimization, and strong generalization. In this study, a ship-based ice load measurement system installed on icebreaker XueLong is introduced. The experimental application is performed to verify the feasibility of the LS_SVM procedure and establish a full-scale ice load identification model. With this method, the identified ice load is reasonably verified through comparative analysis and case study. To estimate the ice load through far-field measurements, the predictive ability of the LS_SVM algorithm can further be applied.