Deep Learning-Based Reconstruction of Vibration Sensor Data for Structural Health Monitoring: A Case Study

Abstract

Monitoring the condition of existing structures remains one of the most pressing challenges within the construction industry. Structural health monitoring (SHM) techniques have proven increasingly effective in this regard; however, maintaining and archiving complete lifecycle data for such structures remains costly. Data acquisition is particularly critical, as the SHM system relies upon this information to analyse and evaluate structural behaviour. Nonetheless, a range of challenges—such as environmental influences, sensor malfunction, and transmission failures—can lead to data corruption or loss. These issues compromise the reliability of the dataset, necessitating either data reconstruction or additional measurement campaigns, both of which are resource-intensive. This study proposes the use of a long short-term memory (LSTM) network to reconstruct missing or corrupted data. A complete dataset collected from an actual construction project is employed to train the network. Data loss scenarios are then simulated, including single-channel (loss from one sensor) and multi-channel (loss from multiple sensors) cases. The trained LSTM model is subsequently applied to reconstruct the missing data. A case study on a real bridge demonstrates that the reconstructed data show strong agreement with the original measurements in both the time and frequency domains. These findings indicate that the proposed approach has the potential to support engineers in conserving resources by reducing the need for costly and time-consuming additional measurement interventions.