Toward a Structural Health Monitoring Methodology for Concrete Structures under Dynamic Loads Using Embedded FBG Sensors and Strain Mapping Techniques

A data-driven-based methodology for SHM in reinforced concrete structures using embedded fiber optic sensors and pattern recognition techniques is presented. A prototype of a reinforced concrete structure was built and instrumented in a novel fashion with FBGs bonded directly to the reinforcing steel bars, which, in turn, were embedded into the concrete structure. The structure was dynamically loaded using a shaker. Superficial positive damages were induced using bonded thin steel plates. Data for pristine and damaged states were acquired. Classifiers based on Mahalanobis' distance of the covariance data matrix were developed for both supervised and unsupervised pattern recognition with an accuracy of up to 98%. It was demonstrated that the proposed sensing scheme in conjunction with the developed supervised and unsupervised pattern recognition techniques allows the detection of slight stiffness changes promoted by damages, even when strains are very small and the changes of these associated with the damage occurrence may seem negligible.

Automated summary

This paper presents a data-driven methodology for structural health monitoring (SHM) in reinforced concrete structures using embedded fiber optic sensors and pattern recognition techniques. A prototype structure was built and instrumented with fiber Bragg gratings (FBGs) bonded directly to the reinforcing steel bars embedded in the concrete. Datasets for pristine and damaged states were acquired, and classifiers based on Mahalanobis' distance were developed for supervised and unsupervised pattern recognition, achieving an accuracy of up to 98%.