Deep Learning‐Based Missing Data Reconstruction in <scp>SHM</scp> Using Gated Dilated Convolution and <scp>GRU</scp>

ABSTRACT Structural health monitoring (SHM) systems often face challenges due to missing or incomplete vibration signals caused by sensor failures, transmission disruptions, or environmental disturbances. To address these issues, we propose WaveNet_GRU, a novel deep learning (DL) model that integrates three key components: dilated causal convolutions for capturing multiscale temporal patterns, gated activation units to enhance nonlinearity and feature selection, and gated recurrent units (GRU) to model long‐term temporal dependencies in structural dynamics. This unified architecture enables WaveNet_GRU to efficiently capture both local fluctuations and global temporal trends in vibration signals. We evaluate the model using two benchmark datasets: a laboratory‐scale physical model of a cable‐stayed bridge and the real‐world Rach Mieu 1 Bridge in Vietnam, under varying missing data rates (10%–30%). Experimental results demonstrate that WaveNet_GRU consistently outperforms baseline models, including standalone GRU and WaveNet‐based architectures. In particular, on the real‐world Rach Mieu 1 Bridge dataset, WaveNet_GRU achieved the highest accuracy with a Coefficient of Determination ( R 2 ) exceeding 0.91 and the lowest Root Mean Square Error (RMSE) under 10% missing data conditions. Furthermore, the model demonstrated superior robustness by maintaining reliable performance even when data loss reached 30%, whereas baseline models exhibited significant degradation. By preserving subtle vibration features during reconstruction, WaveNet_GRU helps maintain continuous monitoring, supporting earlier damage indication and more cost‐effective maintenance planning in practical SHM.