Digital twins (DTs) enhance structural health monitoring (SHM) through accurate simulation and automation. Deep learning (DL) can boost DTs performance, but it relies on both healthy and damaged data, the latter being often scarce. Finite-element modeling (FEM) can supplement this, though aligning simulated data with real-world conditions is key. This paper presents a novel hybrid framework that bridges the gap between simulated and real-world SHM data through a convolutional domain expansion (CDE) technique. A hybrid database integrating FEM simulation data with real accelerometer signals is developed to address the scarcity of damage-state data. Furthermore, a decentralized single-channel deep neural network (SC-DNN) architecture is introduced for multi-damage identification, enhancing flexibility and domain adaptability. Experimental validations on scaled wind turbine structures show that the proposed system achieves over 96% classification accuracy, even under previously unseen damage scenarios. The proposed CDE method increases the cosine similarity between synthetic and real-damaged data from 0.4 to 0.7, demonstrating improved domain transferability. This integrated approach significantly improves generalizability in SHM applications and offers scalable potential for deployment in large civil infrastructures.
Chiang et al. (Thu,) studied this question.