Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring
arXiv:2604.01712v1 Announce Type: new Abstract: 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 rigoro
arXiv:2604.01712v1 Announce Type: new Abstract: 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.
Executive Summary
This article presents a novel transformer-based methodology for wind-induced structural response forecasting and digital twin support in wind structural health monitoring. The proposed approach uses the temporal characteristics of the system to train a forecasting model, allowing for accurate structural behavior capture in realistic conditions and changes in system excitation. The results highlight the potential of transformer-based digital twin components as next-generation tools for resilient infrastructure management, continuous learning, and adaptive monitoring. This approach outperforms existing methods by not requiring assumptions on wind stationarity or structural normal vibration behavior, making it particularly suitable for complex and dynamic systems.
Key Points
- ▸ The proposed methodology uses a transformer-based encoder-decoder architecture for response time series forecasting and digital twin support.
- ▸ The model captures accurate structural behavior in realistic conditions, including changes in system excitation.
- ▸ The approach outperforms existing methods by not requiring assumptions on wind stationarity or structural normal vibration behavior.
- ▸ The methodology has potential applications in resilient infrastructure management, continuous learning, and adaptive monitoring.
Merits
Strength of the Proposed Methodology
The transformer-based encoder-decoder architecture is well-suited for capturing complex temporal relationships in wind-induced structural response data, allowing for accurate forecasting and digital twin support.
Robustness to Non-Stationarity
The proposed methodology does not require assumptions on wind stationarity, making it particularly suitable for complex and dynamic systems.
Potential for Real-World Applications
The results highlight the potential of transformer-based digital twin components as next-generation tools for resilient infrastructure management, continuous learning, and adaptive monitoring.
Demerits
Limited Generalizability
The methodology is trained on a specific dataset from the Hardanger Bridge, and it is unclear whether the results can be generalized to other wind-induced structural response systems.
Computational Complexity
The transformer-based encoder-decoder architecture may require significant computational resources and training time, which could be a limitation for real-world applications.
Interpretability and Explainability
The complex architecture of the proposed methodology may make it challenging to interpret and explain the results, potentially limiting its adoption in real-world applications.
Expert Commentary
The proposed methodology is a significant contribution to the field of wind-induced structural response forecasting and digital twin support. The use of a transformer-based encoder-decoder architecture allows for accurate capture of complex temporal relationships in wind-induced structural response data. However, the limited generalizability of the methodology to other wind-induced structural response systems and the potential for high computational complexity are concerns that need to be addressed. Furthermore, the interpretability and explainability of the results are critical considerations for real-world applications.
Recommendations
- ✓ Further research is needed to investigate the generalizability of the proposed methodology to other wind-induced structural response systems.
- ✓ The computational complexity of the proposed methodology should be reduced through optimization techniques or alternative architectures.
- ✓ The results of the proposed methodology should be compared to other state-of-the-art methods to validate its accuracy and robustness.
Sources
Original: arXiv - cs.LG
