An Echo State Network approach to structural health monitoring

Abstract

Echo State Networks (ESNs) have been applied to time-series data arising from a structural health monitoring multi-sensor array placed onto a test footbridge which has been subjected to a number of potentially damaging interventions over a three year period. The time-series data, sampled approximately every five minutes from ten temperature sensors, have been used as inputs and the ESNs were tasked with predicting the expected output signal from eight tilt sensors that were also placed on the footbridge. The networks were trained using temperature and tilt sensor data up to the first intervention and subsequent discrepancies in the ESNs' prediction accuracy allowed inferences to be made about when further interventions occurred and also the level of damage caused. Comparing the error in signals with the location of each of the tilt sensors allowed damaged regions to be determined.