Comparative study of advance smart strain approximation method using Levenberg-Marquardt and Bayesian regularization backpropagation algorithm

Abstract

This study aimed to develop a smart model prediction of strain calculation using fiber optic sensors and neural network. Optical parameters are obtained experimentally on a cantilever beam structure, under static loading conditions. Five variations are used by creating external damage to study strain variations on healthy, single damage and multiple damage beam structures. The strain values were correlated to the set of phase difference and change in intensities by using feed-forward back propagation neural network approach. The strain values using optical parameters were verified with conventional strain gauge measurement and finite element analysis. The neural network simulation provides advance and more accurate correlation results with strain gauge and FEA analysis. © 2019 Elsevier Ltd. All rights reserved.