Field Validation of a Statistical-Based Bridge Damage-Detection Algorithm

Abstract

This paper describes a field validation of a second-generation, statistical-based damage-detection algorithm and its ability to detect actual damage in bridges accurately. The algorithm had been theoretically validated previously. For the field tests, in lieu of introducing damage to a public bridge, two sacrificial specimens that simulated damage-sensitive locations of the bridge were mounted on the bridge, and different types and levels of damage in the form of cracks and simulated corrosion were induced in the specimens. Using strain data collected from sensors on the sacrificial specimens and on the bridge, the algorithm correctly identified the damage. Analysis of data from sensors far away from the damaged area revealed a relatively high false-positive rate.