Fiber optic sensors (FOS) offer a number of advantages for the purpose of long term Structural
Health Monitoring, such as distributed sensing capability, durability, stability and immunity to electrical
noise. There are different FOS technologies with a wide range of performance metrics that define their suitability
for different applications. One of the most commonly used fiber optic sensing technologies is point sensors
with Fiber Bragg Gratings (FBG) sensors. It is also critical to couple such sensing capabilities with effective
precise data analysis methods that can identify structural changes and detect possible damage. In this
study, robust regression analysis (RRA) is employed to analyze strain data collected with FBG sensors that
are installed on a laboratory 4-span bridge. In order to test the efficiency of this non-parametric data analysis
approach, several tests are conducted with different damage scenarios in the laboratory environment. The efficiency
of both FBG sensors and robust regression algorithm for detection and localizing damage is explored.
Based on the findings presented in this paper, the RRA coupled with fiber bragg grating sensors can be
deemed to deliver promising results to observe and detect both local and global damage implemented on the
structure.