Experimental investigation of surface-mounted optical fibre strain sensor using neural network analysis

Abstract

This study aimed to develop an intelligent neural network perceptron model for strain prediction using fibre optic sensor signals. Optical parameters corresponding to surface-mounted optical fibre are obtained experimentally under static loading conditions. Four variations are used by creating external damage to study strain variations on healthy, single damage, and multiple damage beam structures. The strain values are obtained by using phase difference and change in intensities data as input for the feed-forward back propagation neural network model. A comparative study of pre-existing analytical solutions, conventional strain gauge measurement, and finite element analysis (FEA) is performed. The neural network model provides more accurate correlation results with strain gauge and FEA analysis compared to analytical analysis. Copyright © 2023 Inderscience Enterprises Ltd.