A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder

Over the service life of several decades, structural damage detection is crucial for ensuring the safety and durability of engineering structures. However, existing methods often overlook the spatiotemporal coupling in multi-sensor data, hindering the full exploitation of structural dynamic evolutio...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 25; číslo 21; s. 6724
Hlavní autori:	Ni, Yanchun, Jin, Qiyuan, Hu, Rui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 03.11.2025
Predmet:	Accuracy Bridges Civil engineering damage detection Deep learning Environmental engineering Fault diagnosis graph attention network Identification Localization Methods multi-sensor Neural networks Sensors Social networks structural health monitoring temporal convolutional network unsupervised deep learning Wavelet transforms graph attention network damage detection structural health monitoring multi-sensor unsupervised deep learning temporal convolutional network
ISSN:	1424-8220, 1424-8220
On-line prístup:	Získať plný text
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