Abstract

Interests in strain gauge sensors employing stretchable patch antenna have escalated in the area of structural health monitoring, because the malleable sensor is sensitive to capturing strain variation in any shape of structure. However, owing to the narrow frequency bandwidth of the patch antenna, the operation quality of the strain sensor is not often assured under structural deformation, which creates unpredictable frequency shifts. Geometric properties of the stretchable antenna also severely regulate the performance of the sensor. Especially rugged substrate created by printing procedure and manual fabrication derives multivariate design variables. Such design variables intensify the computational burden and uncertainties that impede reliable analysis of the strain sensor. In this research, therefore, a framework is proposed not only to comprehensively capture the sensor’s geometric design variables, but also to effectively reduce the multivariate dimensions. The geometric uncertainties are characterized based on the measurements from real specimens and a Gaussian copula is used to represent them with the correlations. A dimension reduction process with a clear decision criterion by entropy-based correlation coefficient dwindles uncertainties that inhibit precise system reliability assessment. After handling the uncertainties, an artificial neural network-based surrogate model predicts the system responses, and a probabilistic neural network derives a precise estimation of the variability of complicated system behavior. To elicit better performance of the stretchable antenna-based strain sensor, a shape optimization process is then executed by developing an optimal design of the strain sensor, which can resolve the issue of the frequency shift in the narrow bandwidth. Compared with the conventional rigid antenna-based strain sensors, the proposed design brings flexible shape adjustment that enables the resonance frequency to be maintained in reliable frequency bandwidth and antenna performance to be maximized under deformation. Hence, the efficacy of the proposed design framework that employs uncertainty characterization, dimension reduction, and machine learning-based behavior prediction is epitomized by the stretchable antenna-based strain sensor.

Highlights

  • Structural health monitoring (SHM) is implemented to evaluate the physical conditions of structures with consistent surveillance

  • From Equation (12), two data sets related to the feature (F)’s values can be described as A and B, where n1 and n2 denote the number of the features employed in the categories

  • Once the deformation of 12 mm is applied to the initial antenna, resonance frequency (Rf) drastically shifts, ranging from 2.5 GHz to 2.73 GHz and from 5.05 GHz to 5.23 GHz, which states that the antenna functionality will not be ensured because they deviate from each acceptable Rf range regarding 2.5 and 5.0 GHz

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Summary

Introduction

Structural health monitoring (SHM) is implemented to evaluate the physical conditions of structures with consistent surveillance. To can embrace competent choice of an efficient methodof based on multivariate data properties In eliminate these issues, a rigorous design of stretchable electronics based on efficient capturing and accordance with the entropy-based correlation coefficient, two DR methods, feature modeling correlated high dimensional random variables is of required [24]. Assorted studies have suggested unique shape modification such as conical, pentagon, or even fractal geometry [33,34], but such modifications refuse to preserve an initial outline of the MSP antenna that has advantages of cheaper and easier fabrication process and diverse applicability, so their efficiency is only meaningful in a specialized example To address these issues of the existing design approaches for the stretchable antenna, a design optimization process that includes a structural shape optimization is proposed in this study.

Micro-Strip Patch Antenna
DR-integrated Framework for Optimized Design
Generation of Multivariate Data Random Thickness
Optimization
Predicting Reliability of the Obtained Optimal Design
Stretchable MSP Antenna-Based Strain Sensor
Generation of Multivariate Data for the Varying Thickness
DR Process for Variation of the Substrate Thickness
ANN-Based Surrogate Model to Predict Antenna Deformation
Dimension Reduction of Coordinates of the Patch
Design of the the MSP
2.73 GHz and from Displacement
GHz with
Findings
Conclusions
Full Text
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