No abstract

No abstract

An apodized FBG is designed to investigate the impacts of side lobe elimination in the quasi-distributed sensing for the estimation of measurands (like temperature and strain) to assess the condition of any 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 high range of side lobes in the quasi-distributed sensing network. Therefore, elimination of side lobe is highly necessary by introducing the method of apodization. The sensitivity of designed apodized FBG is estimated by analyzing the variations in the Bragg wavelength due to the impacts of temperature and strain. The Bragg wavelength changes depending on the measurands could 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 the temperature and the strain. To measure the physical parameter effectively, it is very much 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 applications as both the measurands can affect the Bragg wavelength. In this work, the Machine Learning (ML) methods (such as Support Vector Machine, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Decision Tree, and Ensemble model) are introduced to differentiate the effects of temperature and strain on a single Bragg wavelength shift measurement. The Artificial Neural Network (ANN) is used for the predictive analysis of physical parameters to realize any detrimental scenario of any measurands. It has been noted that the performance of the proposed Ensemble model is higher compared to other models for the classification of the temperature and the strain.

No abstract

No abstract