Deep learning methods have recently shown great success in numerous fields, including finance, healthcare, linguistics, robotics and even cybersports. Unsupervised learning methods identify the dominant patterns of variability that shape a data set. Such patterns may correspond to well-understood processes, previously unknown clusters or anomalies. This paper presents a case study where a state-of-the-art family of unsupervised deep learning models called variational autoencoder (VAE) is applied to data accrued from a network of fibre-optic sensors installed within a composite steel–concrete half-through railway bridge. The goals were (a) to characterise automatically the behaviour of the bridge based on sensor measurements and, (b) based on this characterisation, to determine when a train passes across a bridge. Based on the VAE model, an algorithm is presented to identify automatically the ‘train event’ points in an unsupervised setting. Two architectures for the VAE model are compared with commonly used baselines. The architecture tailored for modelling sequential data is shown to outperform other methods considered, on both seen and unseen data. No special hyperparameter optimisation is required. This study illustrates how state-of-the-art deep learning methods can be applied to a civil infrastructure engineering problem without directly modelling the physics of the objects or performing tedious hyperparameter optimisation.