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 on-site under low-amplitude ambient excitation. Finally, the applicability of this method in the scenarios of modal identification and sensor validation is demonstrated.

结构状态感知是当代结构健康监测的关键步骤。传感器故障和欠感严重阻碍了结构健康监测系统的性能。本文从序列数据生成的角度，使用具有软注意力机制的序列到序列建模框架，提出了一种用于结构健康监测的新型结构动态响应重建和虚拟传感方法。该框架明确地利用了序列数据中潜在的时空相关性，促进了网络中信息的高效流动，从而显着提高了重构性能。此外，还提出了一种基于以熵为特征的信号复杂度的重构误差估计和不确定性量化方法。基于在低振幅环境激励下现场测量的人行桥振动信号，验证了所提出方法的有效性和鲁棒性。最后，证明了该方法在模态识别和传感器验证场景中的适用性。