Graph neural network-based diagnosis of complex damages in traditional timber structures

Abstract:

To address the limitations of traditional timber structure damage identification algorithms in parameter space completeness, utilization efficiency of high-dimensional data, and integrated local-global damage diagnosis capabilities, this paper proposes a multi-modal data fusion damage identification method based on a graph neural network (GNN). First, with the acceleration response of structural nodes and material parameters as inputs, the graph structure data is constructed by fusion of sensor topological relations to realize the interactive transmission and collaborative identification of damage features between nodes. Second, this paper proposes a joint recognition algorithm, which is composed of a local damage identification network (LCGCN-LDI) and a global material deterioration network (GCN-MPI), to identify the damage of nodes and correct the material property parameters respectively. Finally, experimental validation shows that the joint identification algorithm achieves an overall structural damage recognition accuracy of 94.7%, and a local damage detection accuracy of 97.6%; the prediction error of natural vibration frequency is reduced from 28.9% to 9.4%. The results show that the joint recognition algorithm outperforms traditional algorithms in identifying complex damage in traditional timber structures, exhibiting high accuracy and robustness.