Development of a structural health monitoring methodology in reinforced concrete structures using FBGs and pattern recognition techniques

Structural health monitoring (SHM) is a branch of structural engineering which seeks for the development of monitoring systems that provide relevant information of any alteration that may occur in an engineering structure. This work presents the implementation of an SHM methodology in a prototype structure made of reinforced concrete by using fiber Bragg gratings (FBGs), a type of fiber optic sensor capable of measuring strain and temperature changes due to external stimuli. The SHM system includes an interrogation device and signal processing algorithms which are intended to study the physical variations on the FBGs measurements in order to detect anomalies in the structure promoted by a damage occurrence. The structure prototype is a porticoed structure which contains 48 embedded sensors: 32 of them are destinated for the strain measurement and are located in both columns and beams of the structure, 16 are temperature sensors which have been embedded for thermal compensation. Strain datasets for both pristine and damaged conditions were obtained for the structure while it was excited with a mechanical shaker which induced dynamic loading conditions resembling earthquakes. By using classification algorithms based on pattern recognition, it is intended to process the datasets with the aim of reaching the first level of SHM in the structure (damage detection).