Structural dynamic response reconstruction and virtual sensing using a sequence to sequence modeling with attention mechanism

Structural condition perception is a crucial step in contemporary structural health monitoring. Sensor malfunction and under-sensing seriously hamper the performance of the structural health monitoring system. This paper proposes a novel structural dynamic response reconstruction and virtual sensing approach for structural health monitoring using a sequence-to-sequence modeling framework with a soft attention mechanism from the perspective of sequence data generation. This framework explicitly utilizes the potential spatiotemporal correlation in sequence data and promotes the efficient flow of information in the network, thereby significantly improving the reconstruction performance. In addition, a reconstruction error estimation and uncertainty quantification method based on signal complexity characterized by entropy is also developed. The effectiveness and robustness of the proposed method are verified based on the vibration signals of a footbridge measured onsite under low-amplitude ambient excitation. Finally, the applicability of this method in the scenarios of modal identification and sensor validation is demonstrated.

Automated summary

This paper proposes a novel method for structural health monitoring based on a sequence-to-sequence modeling framework, utilizing soft attention mechanism and signal complexity characteristics to improve reconstruction performance effectively. The effectiveness and robustness of the method are verified through onsite vibration signal experiments, showcasing its applicability in scenarios of modal identification and sensor validation.