# Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring

arXiv: https://arxiv.org/abs/2604.01712 · HTML: https://mlsanity.com/paper/2604.01712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 90 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform](https://mlsanity.com/paper/2101.04516/) (2021)
- [Foundation Models for Structural Health Monitoring](https://mlsanity.com/paper/2404.02944/) (2024)
- [Transformer-Based Denoising of Mechanical Vibration Signals](https://mlsanity.com/paper/2308.02166/) (2023)
- [Monitoring-Supported Value Generation for Managing Structures and Infrastructure Systems](https://mlsanity.com/paper/2402.00021/) (2024)
- [A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation](https://mlsanity.com/paper/2511.00099/) (2025)

## Built on by (most cited first, 1 of 1)

- [Recursive transformers for semiconductor thermo-mechanical reliability](https://mlsanity.com/paper/2607.27251/) (2026)

## Abstract (arXiv)

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.