Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914439485194240 |
|---|---|
| author | Zhou, Feiyu Impraimakis, Marios |
| author_facet | Zhou, Feiyu Impraimakis, Marios |
| contents | The wind-induced structural response forecasting capabilities of a novel transformer methodology are examined here. The model also provides a digital twin component for bridge structural health monitoring. Firstly, the approach uses the temporal characteristics of the system to train a forecasting model. Secondly, the vibration predictions are compared to the measured ones to detect large deviations. Finally, the identified cases are used as an early-warning indicator of structural change. The artificial intelligence-based model outperforms approaches for response forecasting as no assumption on wind stationarity or on structural normal vibration behavior is needed. Specifically, wind-excited dynamic behavior suffers from uncertainty related to obtaining poor predictions when the environmental or traffic conditions change. This results in a hard distinction of what constitutes normal vibration behavior. To this end, a framework is rigorously examined on real-world measurements from the Hardanger Bridge monitored by the Norwegian University of Science and Technology. The approach captures accurate structural behavior in realistic conditions, and with respect to the changes in the system excitation. The results, importantly, highlight the potential of transformer-based digital twin components to serve as next-generation tools for resilient infrastructure management, continuous learning, and adaptive monitoring over the system's lifecycle with respect to temporal characteristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01712 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring Zhou, Feiyu Impraimakis, Marios Machine Learning Artificial Intelligence Signal Processing Computational Physics 68T07, 68T05, 68T09, 68T10, 68T30, 37M10, 37N30, 65L05, 65L20, 65M06, 65M20, 65N06, 65N22, 93C10, 93C20, 93C35, 60G25, 60G35, 62M10, 90C30, 76D05, 35Q30, 74H45, 86A10 I.2.6; I.5.1; I.5.4; I.6.1; I.6.3; I.6.4; I.6.5; I.6.7; I.6.8; I.2.8; I.2.10; H.2.8; H.3.3; G.3; G.1.2; C.3; C.4; J.2; J.6; K.6.1; F.1.1; F.2.1; D.2.2; D.2.8; E.1; B.4.1 The wind-induced structural response forecasting capabilities of a novel transformer methodology are examined here. The model also provides a digital twin component for bridge structural health monitoring. Firstly, the approach uses the temporal characteristics of the system to train a forecasting model. Secondly, the vibration predictions are compared to the measured ones to detect large deviations. Finally, the identified cases are used as an early-warning indicator of structural change. The artificial intelligence-based model outperforms approaches for response forecasting as no assumption on wind stationarity or on structural normal vibration behavior is needed. Specifically, wind-excited dynamic behavior suffers from uncertainty related to obtaining poor predictions when the environmental or traffic conditions change. This results in a hard distinction of what constitutes normal vibration behavior. To this end, a framework is rigorously examined on real-world measurements from the Hardanger Bridge monitored by the Norwegian University of Science and Technology. The approach captures accurate structural behavior in realistic conditions, and with respect to the changes in the system excitation. The results, importantly, highlight the potential of transformer-based digital twin components to serve as next-generation tools for resilient infrastructure management, continuous learning, and adaptive monitoring over the system's lifecycle with respect to temporal characteristics. |
| title | Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring |
| topic | Machine Learning Artificial Intelligence Signal Processing Computational Physics 68T07, 68T05, 68T09, 68T10, 68T30, 37M10, 37N30, 65L05, 65L20, 65M06, 65M20, 65N06, 65N22, 93C10, 93C20, 93C35, 60G25, 60G35, 62M10, 90C30, 76D05, 35Q30, 74H45, 86A10 I.2.6; I.5.1; I.5.4; I.6.1; I.6.3; I.6.4; I.6.5; I.6.7; I.6.8; I.2.8; I.2.10; H.2.8; H.3.3; G.3; G.1.2; C.3; C.4; J.2; J.6; K.6.1; F.1.1; F.2.1; D.2.2; D.2.8; E.1; B.4.1 |
| url | https://arxiv.org/abs/2604.01712 |