AI & Machine Learning

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

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A new transformer model using self-attention encoder-decoder and multimodal deep learning has been developed for wind-induced structural response forecasting in bridge health monitoring. This AI-based method excels by not requiring assumptions about stationary wind conditions or normal vibration behavior, making it highly adaptable to changing environmental factors. The model's effectiveness is demonstrated through real-world data from the Hardanger Bridge, showcasing its potential as a robust tool for infrastructure management and continuous learning.

Read the full article at arXiv cs.LG (ML)

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