Hybrid One-Dimensional Convolutional Neural Network—Recurrent Neural Network Model for Reconstructing Missing Data in Structural Health Monitoring Systems

Abstract

Data loss is a recurring and critical issue in Structural Health Monitoring (SHM) systems, often arising from a range of factors including sensor malfunction, communication breakdown, and exposure to adverse environmental conditions. Such interruptions in data availability can significantly compromise the accuracy and reliability of structural performance assessments, thereby hindering effective decision-making in safety evaluation and maintenance planning. In this study, a novel deep learning-based framework is proposed for data reconstruction in SHM, employing a hybrid architecture that integrates one-dimensional convolutional neural networks (1D-CNNs) with recurrent neural networks (RNNs). By combining these complementary strengths, the hybrid 1D-CNN–RNN model demonstrates superior capacity for accurate signal reconstruction. A real-world case study was conducted using vibration data from the Trai Hut Bridge in Vietnam. Five network configurations with varying depths were examined under single- and multi-channel loss scenarios. The results confirm that the method can accurately reconstruct lost signals. For single-channel loss, the best configuration achieved an MAE = 0.019 m/s2 and R2 = 0.987, while for multi-channel loss, a deeper network yielded an MAE = 0.044 m/s2 and R2 = 0.974. Furthermore, the model exhibits robust and stable performance even under more demanding multi-channel data loss conditions, highlighting its resilience to practical operational challenges. The results demonstrate that the proposed CNN–RNN framework is accurate, robust, and adaptable for practical SHM data reconstruction applications.