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

Authors: A.S. Wali; Amit Tyagi

Addresses: Department of Mechanical Engineering, Indian Institute of Technology, (Banaras Hindu University), Varanasi 221005, India ' Department of Mechanical Engineering, Indian Institute of Technology, (Banaras Hindu University), Varanasi 221005, India

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.

Keywords: optical parameters; neural network; FEA; finite element analysis; strain.

DOI: 10.1504/IJMSI.2023.135869

International Journal of Materials and Structural Integrity, 2023 Vol.15 No.1, pp.1 - 14

Received: 26 Jun 2019
Accepted: 31 Dec 2019
Published online: 09 Jan 2024 *

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