Structural Health Monitoring by Fiber Optic Sensors

Although a crack creates a significant strain field at its tip, its effect on the strain field becomes nearly negligible only a few centimeters away from the crack, which complicates the task of damage detection. Two approaches are currently in use. The first one is a local approach that can detect damage if it intersects the optical fiber path; it is straightforward to implement but is limited to cases where the potential damage location can be anticipated (for example, in a concrete beam under flexural loads or around aircraft cargo doors). The second one, a global approach, seeks to identify damage anywhere in the structure by detecting subtle changes in the field of global strain. There is a need for algorithms to compare the strain dataset before and after damage. Machine learning offers tools to achieve this, but these tools have to be carefully selected to achieve good damage detectability. In this paper, we compare algorithms based on multivariate data analysis as well as data processing using neural networks, comparing their performance on a real structure.