Digital twins (DTs) provide real-time understanding of structural response by coupling measurements with continuously updated models; this capability supports structural health monitoring (SHM) by enabling timely condition assessment, anomaly detection, and decision support. This study presents convolutional neural network (CNN)-based surrogates for predicting structural behavior within a DT framework, with a focus on improving surrogate model generalization and robustness. As traditional finite element (FE) models remain computationally demanding, limiting real-time SHM, this work introduces deep learning surrogates that replace FE simulations to enable faster analysis. Two existing CNN architectures, SCSNet and StressNet, are utilized to train on diverse datasets representing 2D plate structures subjected to varying loads and boundary conditions. StressNet, which uses multi-channel input encoding, outperforms SCSNet in predictive accuracy and robustness, particularly on unseen scenarios. This highlights the importance of dataset diversity in enhancing generalization. To demonstrate the integration of such surrogates into a full DT framework, the study also incorporates an iterative updating strategy that refines model inputs using displacement (structural behavior measurements). This component, supported by a set of CNN-based “calculators,” allows bidirectional learning between physical twin measurements and digital twin input, enabling continuous model updating. An inverse problem example is adopted to present the practice of a full DT framework. Together, these forward and updating components form a cohesive DT framework that links physical observations with virtual predictions. The results show significant promise for using CNNs to achieve efficient and accurate structural response prediction, paving the way for near real-time SHM and improved infrastructure decision-making through data-driven digital twin technologies.
Zhiyanpour et al. (Sat,) studied this question.