Generalization of anomaly detection in bridge structures using a vibration-based Siamese convolutional neural network

Corrosion is one of the main damages in steel bridges, which appears as a loss of material and sectional area and causes member failure over time. A reliable bridge management system not only should help in preventing catastrophic structural failure by employing an in-time anomaly detection approach for all the bridges within a network but also should reduce overall network costs commonly raised by expensive inspections. This paper proposes a deep learning approach to generalize anomaly detection due to section losses in steel bridges based on Siamese convolutional neural network (SCNN). A series of steel beams and bridges with various cross-sections and lengths are considered to examine the performance of SCNN in generalizing anomaly detection in these structures. The study considered data from finite element simulations and experiments. The results reveal that the proposed integrated SCNN can detect anomalies successfully according to Australian standard AS7636 with reasonably high accuracy.