Thermal feature subspace based deep learning framework for bridge girder-end displacement reconstruction

Expansion displacement at the girder-end is a significant indicator of the thermal effects on long-span bridges. To address the issue of missing or anomalous bridge girder-end displacement (GED) data caused by sensor failures or transmission interruptions in structural health monitoring (SHM), a fea...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Miao Xu, Guang Qu, Limin Sun, Bing Li, Ye Xia, Yexiang Yan, Jianping Han, Yongfeng Du
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Elsevier 2026-01-01
Sarja:	Case Studies in Thermal Engineering
Aiheet:	Temperature-displacement correlation Structural health monitoring (SHM) Girder-end displacement (GED) Data reconstruction Hybrid LSTM-Transformer model
Linkit:	http://www.sciencedirect.com/science/article/pii/S2214157X2501740X
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