Smoothly Clipped Absolute Deviation Low‐Rank Informed Temporal Convolutional Generative Adversarial Imputation Network for Simultaneous Continuous Missing Data Imputation in Structural Dynamic Monitoring

ABSTRACT Structural dynamic response time histories required for seismic analysis are particularly susceptible to continuous data missing occurring simultaneously across all sensor channels during seismic hazard. Earthquake‐induced disruptions often induce power interruptions and communication failures, and thus may delay analytical procedures or render structural evaluation infeasible. Although recent deep learning imputation methods can capture spatiotemporal correlations, they require some completely observed measurements and therefore fail to recover fully missing time intervals across all channels. Conversely, matrix completion methods exploit intrinsic low‐rank characteristics of structural dynamic responses. However, the temporal evolution of structural dynamics is not explicitly encoded in their formulations, and convex relaxations tend to introduce over‐shrinkage and estimation bias. Therefore, this study proposes a smoothly clipped absolute deviation (SCAD) low‐rank informed generative adversarial imputation network with an enhanced temporal convolutional generator for the challenging problem of continuous and simultaneous missing data imputation in structural dynamic responses. The physics‐informed low‐rank constraint is embedded into the generative process to capture global inter‐sensor correlation structures. Meanwhile, the temporal convolutional network (TCN) generator explicitly models long‐range temporal dependencies, which supports the reconstruction of nonlinear vibration characteristics across extended missing intervals. Adversarial learning constrains the imputed responses to be statistically consistent with the observed data. The proposed method is validated through two real‐world case studies of a bridge and the Canton Tower under ambient vibration or in‐situ seismic excitation. Ablation studies are conducted to examine the individual contributions of the enhanced TCN generator and the SCAD low‐rank constraint. In addition, comparative studies with state‐of‐the‐art approaches demonstrate the reconstruction accuracy and computational efficiency, highlighting its practical potential for rapid post‐earthquake structural assessment.