Title

Implementation Of Robust Regression Algorithm (Rra) To Detect Structural Change Using Fiber Bragg Grating (Fbg) Data

Abstract

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. © 2012 Taylor & Francis Group.

Publication Date

1-1-2012

Publication Title

Bridge Maintenance, Safety, Management, Resilience and Sustainability - Proceedings of the Sixth International Conference on Bridge Maintenance, Safety and Management

Number of Pages

338-345

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1201/b12352-42

Socpus ID

84863890365 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84863890365

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