An LSTM Autoencoder-Based Approach for Monitoring Railway Bridges

Continuous monitoring of railway bridges is essential for ensuring safety and operational reliability, considering aging mechanisms, rising traffic, and elevated speeds of railway vehicles. Frequently, traditional vibration-based approaches, including modal identification and data-driven diagnostic strategies, are strongly influenced by environmental and operational variability, requiring labeled damaged datasets or numerical simulations to provide reliable outcomes. However, the acquisition of complete and representative datasets for training neural networks in structural health monitoring remains a challenging task, particularly for large-scale civil structures such as bridges. In these cases, unsupervised learning approaches represent promising solutions. An unsupervised anomaly detection methodology for railway bridge monitoring based on a long short-term memory (LSTM) autoencoder (AE) trained exclusively on bridge accelerations under healthy structural conditions is proposed in the present work. Specifically, the acceleration responses are obtained from simulations made on a calibrated finite element model of the bridge, reproducing realistic train–bridge interaction scenarios. The multi-channel acceleration signals are reconstructed by the proposed LSTM AE to produce the Root Mean Square Error (RMSE) between measured and reconstructed acceleration responses as indicators of potential structural anomalies. A dual-threshold strategy is adopted for damage detection purposes, including a global threshold for identifying anomalies in the overall dynamic response and per-sensor thresholds derived from the healthy-condition RMSE distribution for detecting localized damages. Only healthy-condition data are required for employing the proposed technique, avoiding labeled damaged data for training purposes. The LSTM AE constitutes an effective and computationally efficient tool for anomaly detection and continuous structural health monitoring of railway bridges, as demonstrated by the obtained results, representing a promising alternative to classical modal-based approaches and existing deep learning-based methods.