Structural Damage Identification Based on a Multi-Scale Adaptive Fusion Graph Convolutional Neural Network

NI Yanchun; JIN Qiyuan; HU Rui

doi:10.3724/j.gyjzG25041503

Volume 55 Issue 12

Dec. 2025

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NI Yanchun, JIN Qiyuan, HU Rui. Structural Damage Identification Based on a Multi-Scale Adaptive Fusion Graph Convolutional Neural Network[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(12): 188-197. doi: 10.3724/j.gyjzG25041503

Citation:

NI Yanchun, JIN Qiyuan, HU Rui. Structural Damage Identification Based on a Multi-Scale Adaptive Fusion Graph Convolutional Neural Network[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(12): 188-197. doi: 10.3724/j.gyjzG25041503

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Structural Damage Identification Based on a Multi-Scale Adaptive Fusion Graph Convolutional Neural Network

doi: 10.3724/j.gyjzG25041503

School of Civil Engineering，Tongji University，Shanghai 200092，China

Received Date: 2025-04-15
Available Online: 2026-01-06
Publish Date: 2025-12-20

Abstract

Abstract

Graph Convolutional Networks （GCNs） have attracted significant attention in structural damage identification due to their capacity to explicitly model relations among variables from different sensors. However， existing GCN-based methods predominantly rely on static graphs or single-scale dynamic graph structures， often failing to effectively extract both local and global features. To address this limitation， this study proposes a structural damage identification method using a Multi-scale Adaptive Fusion Graph Convolutional Neural Network （MAFGCN）. Firstly， a physics-informed dynamic graph structure was constructed based on Proper Orthogonal Decomposition （POD）， while a static graph structure was established using the spatial topology of the sensors. Secondly， simplified graph convolution was employed to extract multi-scale features from the dynamic graphs， which were then fused with the static graph features. Finally， a multi-scale heterogeneous graph fusion framework was designed， incorporating an adaptive weighting mechanism to fuse the multi-scale adjacency matrices， thereby enabling the extraction of high-order spatial features across different scales. The proposed method was validated through a numerical simulation of a simply-supported beam and an experiment on a Qatar Stadium stand. Additionally， its noise robustness was verified by introducing varying levels of noise into the original vibration data. The results demonstrated that the proposed method effectively extracted multi-scale features of the vibration data， achieving a damage identification accuracy exceeding 90% even under a 15% noise level.
- graph convolution,
- neural network,
- multi-scale features,
- Proper Orthogonal Decomposition,
- damage identification

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References(21)

References

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