Time series data recovery in SHM of large-scale bridges: Leveraging GAN and Bi-LSTM networks

Abstract

This paper proposes the utilization of a Bidirectional Long Short-Term Memory (Bi-LSTM) network and a Generative Adversarial Network (GAN) model, to recover measured time-series data from Structural Health Monitoring (SHM) systems. In civil engineering, time-series data plays a crucial role in SHM systems. However, unforeseen incidents, such as equipment malfunctions, flawed data collection procedures, or human errors may result in missing data or adversely affect the accuracy of the data collected. To address this challenge, researchers have recently introduced various methods for time series data imputation. Nevertheless, it is evident that many existing approaches are hindered by inherent limitations: (1) neglecting bidirectional temporal correlations; (2) failing to model correlations among variables; (3) generating data that inadequately reflects the distribution of the original dataset, leading to inaccuracies in the recovered data. To overcome these limitations, this study proposes the utilization of Bi-LSTM-GAN for recovering time-series data in the context of SHM. Bi-LSTM demonstrates remarkable proficiency in capturing bidirectional temporal and cross-variable correlations, while GAN is utilized to precisely acquire the distribution of the original data. Additionally, Bi-LSTM significantly bolsters the capacity for long-term data recovery in contrast to a conventional Recurrent Neural Network (RNN). To evaluate the efficacy of this approach, we employ two real-world models: a laboratory-based cable-stayed bridge and an actual three-span continuous bridge. The obtained results compellingly demonstrate that Bi-LSTM-GAN not only achieves data restoration effectively but also yields superior accuracy when compared to conventional GAN model.