Instrumentation of infrastructure is changing the way engineers design, construct, monitor and maintain structures such as roads, bridges and underground structures. Data gathered from these instruments have changed the hands-on assessment of infrastructure behaviour to include data processing and statistical analysis procedures. Engineers wish to understand the behaviour of the infrastructure and detect changes, e.g. degradation, but now using high frequency data acquired from a sensor network.

Presented is a case study that models and analyses in real-time, the dynamic strain data gathered from a railway bridge which has been instrumented with fibre-optic sensor networks. The high frequency of the data combined with the large number of sensors requires methods that efficiently analyse the data. First, automated methods are developed to extract train passage events from the background signal and underlying trends due to environmental effects. Second, a streaming statistical model which can be updated efficiently is introduced that predicts strain measurements forward in time.

This tool is enhanced to provide anomaly detection capabilities in individual sensors and the entire sensor network. These methods allow for the practical processing and analysis of large data sets. The implementation of these contributions will be essential for demonstrating the value of self-sensing structures.

Citation information

Lau, F.D.H., Butler, L.J., Adams, N.M., Elshafie, M.Z. and Girolami, M.A., 2018. Real-time statistical modelling of data generated from self-sensing bridges. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, pp.1-42.

Turing affiliated authors