  Structural Health Monitoring (SHM) plays an indispensable role in ensuring
the longevity and safety of infrastructure. With the rapid growth of sensor
technology, the volume of data generated from various structures has seen an
unprecedented surge, bringing forth challenges in efficient analysis and
interpretation. This paper introduces a novel deep learning algorithm tailored
for the complexities inherent in multimodal vibration signals prevalent in SHM.
By amalgamating convolutional and recurrent architectures, the algorithm
adeptly captures both localized and prolonged structural behaviors. The pivotal
integration of attention mechanisms further enhances the model's capability,
allowing it to discern and prioritize salient structural responses from
extraneous noise. Our results showcase significant improvements in predictive
accuracy, early damage detection, and adaptability across multiple SHM
scenarios. In light of the critical nature of SHM, the proposed approach not
only offers a robust analytical tool but also paves the way for more
transparent and interpretable AI-driven SHM solutions. Future prospects include
real-time processing, integration with external environmental factors, and a
deeper emphasis on model interpretability.
