Theoretical investigation of the impact of apodized fiber Bragg grating and machine learning approaches in quasi-distributed sensing

Abstract

An apodized fiber Bragg grating (FBG) is designed to investigate the impacts of side lobe elimination in quasi-distributed sensing for the estimation of measurands (like temperature and strain) to assess the condition of civil structures, such as bridges. The adjacent FBG spectrums may overlap with each other because of the impacts of temperature and strain due to the presence of a high range of side lobes in a quasi-distributed sensing network. Therefore, elimination of side lobes is necessary, by introducing a method of apodization. The sensitivity of the designed apodized FBG is estimated by analyzing the variations in the Bragg wavelength due to the impacts of temperature and strain. The changes in Bragg wavelength due to the measurands can affect the grating period and the grating index of the FBG. The period of the grating and the grating index of the FBG are simultaneously varied by temperature and strain. To measure the physical parameters effectively, it is essential to distinguish whether the changes in the Bragg wavelength are owing to the impacts of temperature or to the impacts of strain. The effect of cross-sensitivity between the temperature and the strain is a key problem in any FBG-based sensing application as both the measurands can affect the Bragg wavelength. In this work, machine learning methods (the support vector machine, K-nearest neighbors, logistic regression, naïve Bayes, decision tree, and ensemble models) are introduced to differentiate between the effects of temperature and strain on a single Bragg wavelength shift measurement. An artificial neural network is used for the predictive analysis of physical parameters, to identify any measurements of potential concern. It has been noted that the performance of the proposed ensemble model is higher compared to other models for the classification of temperature and strain.