Numerical evaluation of multi-metric data fusion based structural health monitoring of long span bridge structures

Abstract

This work focuses on structural health monitoring of long span bridges for damage detection. A feature extraction level data fusion based damage isolation strategy is presented using multi-metric sensing. The multi-metric sensing uses two types of sensors, namely strain sensors and accelerometers. The methodology combines the advantages offered by each type of sensors, while at the same time overcomes their limitations. The flexibility index method is applied and the flexibility matrices based on the strain and displacement data are combined after performing co-ordinate transformation. A study has been carried out on a simulated finite element model of the Great Belt East Bridge where realistic damage scenarios like damage in the girder, breaking of hanger cables, pier settlement, and loss of cable pretension were introduced on the structure. The study indicates that multi-metric sensing is indeed necessary as it reduces the possibility of false detections and increases the sensitivity and robustness of the methodology.