Ultimate strength assessment of ship hull plate with multiple cracks under axial compression using artificial neural networks

Abstract

Structural capability evaluation of ship hull plate subjected to multiple cracks damage is of profound importance for the inspection and maintenance programs of aged ships. In the current paper, ultimate strength of the ship plate with multiple cracks under axial compression is investigated by using nonlinear finite element method (FEM) and artificial neural network (ANN). Influences of orientation angles, lengths, longitudinal positions and transverse positions of the multiple cracks as well as the plate slenderness ratio on the strength characteristics are elaborated based on series of parametric analyses. According to the numerical results, the intricate behaviors of ultimate strength reduction are caused by the coupling effect of these influential factors. As the characteristics demonstrate high nonlinearity against the calculated parameters, ANN is applied to develop the empirical formulation for predicting the residual strength of ship plate with multiple cracks on the basis of 11850 numerical results. Additional independent database is used to validate the accuracy and applicability of the suggested approach.