Efficient model updating of shaft-raft-hull system using multi-stage convolutional neural network combined with sensitivity analysis

Model updating for marine shaft-raft-hull systems presents significant challenges due to the numerous components and the resulting inaccessible parameters. This study introduces an advanced framework for updating the model parameters of these complex coupling systems using convolutional neural networks (CNNs). Rapid analysis technique based on the substructure synthesis method is employed to enhance the efficiency of generating the CNN training dataset. Global sensitivity analysis is then utilized to identify critical parameters across various frequency bands. Informed by parameter sensitivity, the frequency bands are segmented, and a multi-stage CNN model updating strategy is proposed. The effectiveness of this method is validated through numerical simulations and experimental studies on a scaled shaft-raft-hull model. The findings demonstrate that segmenting the dataset based on global sensitivity analysis results markedly improves the convergence of CNNs, providing a robust solution for model updating in complex marine systems. •Introduced a CNN-based model updating framework for shaft-raft-hull systems.•Utilized substructure synthesis and sensitivity analysis to enhance CNN.•Proposed a multi-stage CNN strategy informed by global sensitivity analysis.•Validated method through numerical simulations and experimental studies.•Demonstrated improved convergence and accuracy in model updating.