Structural Shape Estimation by Mode Shapes Using Fiber Bragg Grating Sensors: A Genetic Algorithm Approach

Abstract

Structural shape estimation is of great interest in many engineering fields including aerospace and civil engineering. During operation, however, the monitoring of structural displacements is often difficult. This article discusses structural shape estimation using a minimal number of fiber Bragg grating sensors. A strain to displacement transformation matrix is derived using mode shapes, to estimate the global displacement of a structure from measured discrete strain data. The number of sensors and sensor layout for the shape estimation is optimized using genetic algorithm. Static and dynamic displacement experiments are conducted on an aluminum plate to verify the algorithm. The deformation during static experiments is measured at nine different locations and estimation error was less than 0.5%. The deformation during dynamic excitation is measured at one selected location using a noncontact laser sensor. The shape estimation quality is better for resonance frequencies compared to off-resonance frequencies. The results show that the estimated displacements match well with those measured displacements.