Adaptive GN block-based model for seismic response prediction of train-bridge coupled systems

Abstract

Deep learning (DL) methods has found extensive application in predicting structural responses. However, conventional neural networks often lack the capacity to effectively capture inter-data relationships, relying solely on training data for end-to-end prediction. Consequently, these methods may struggle with generalization, particularly when faced with new structural configurations absent from the training dataset. To overcome this limitation, this study adopts a graph-based framework, representing structures as graphs facilitates the transfer of features between different components, mimicking the transfer of forces in the real world. Subsequently, a Graph Neural Network Blocks (GN Blocks) model is introduced. Leveraging the holistic inference structure inherent in graph representations, the GN Block-based model as an adaptive model exhibits superior generalization performance in response analysis tasks involving unknown structural forms. Illustrated through the application to train-bridge coupled (TBC) systems, the GN Block-based model demonstrates high prediction accuracy and robust generalization performance, underscoring its suitability for tackling complex structural response prediction.