Loading Localization by Small-Diameter Optical Fiber Sensors

Abstract

Structural health monitoring (SHM) in service has attracted increasing attention for years. Load localization on a structure is studied hereby. Two algorithms, i.e., support vector machine (SVM) method and back propagation neural network (BPNN) algorithm, are proposed to identify the loading positions individually. The feasibility of the suggested methods is evaluated through an experimental program on a carbon fiber reinforced plastic laminate. The experimental tests involve in application of four optical fiber-based sensors for strain measurement at discrete points. The sensors are specially designed fiber Bragg grating (FBG) in small diameter. The small-diameter FBG sensors are arrayed in 2-D on the laminate surface. The testing results indicate that the loading position could be detected by the proposed method. Using SVM method, the 2-D FBG sensors can approximate the loading location with maximum error less than 14 mm. However, the maximum localization error could be limited to about 1 mm by applying the BPNN algorithm. It is mainly because the convergence conditions (mean square error) can be set in advance, while SVM cannot.