Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning

Pham-The Hien,Ic-Pyo Hong

doi:10.3390/app112210682

Abstract

Wall-thinning in building structures due to corrosion and surface erosion occurs due to the severe operating conditions and the changing of the surrounding environment, or it can result from poor workmanship and a lack of systematic monitoring during construction. Hence, the continuous monitoring of structures plays an important role in decreasing unexpected accidents. In this paper, a novel method based on the deep neural network and support vector machine approaches is investigated to build up a thickness classification model by incorporating different input features, including the dielectric constants of the material under test, which are extracted from the scattering parameters proceeded by the National Institute of Standards and Technology iterative method. The attained classification results from both machine learning algorithms are then compared and show that both of the models have a good prediction ability. While the deep neural network is the better solution with a large amount of data, the support vector machine is the more appropriate solution when employing small dataset. It can be stated that the proposed method is able to support systematic monitoring as it can help to improve the accuracy of the prediction of material thickness.

Highlights

According to international norms and regulations, any building materials, products, and elements should have specific physical and strength properties to ensure that the required ultimate and serviceability limit states are met in designed buildings over their whole useful lives [1]
The same amount of data in the training set, validation set, and testing set were used in the training phase, validation, and testing phase for both deep neural network (DNN) and support vector machine (SVM) classification models in this paper
Validation set, and testing set were used in the training phase, validation, and testing phase for both DNN and SVM classification models in this paper

Summary

Introduction

According to international norms and regulations, any building materials, products, and elements should have specific physical and strength properties to ensure that the required ultimate and serviceability limit states are met in designed buildings over their whole useful lives [1]. The material thickness classification models were established by taking the new training dataset extracted from the initial scattering parameters of free space measurement simulations including five input features in the frequency domain: relative permittivity, loss tangent, real and imaginary parts of S21, and frequency. Thereafter, by incorporating five inputs including frequency, relative permittivity, loss tangent, and real and imaginary parts of S21 into the training phase, the thickness classification models are established Sci. 2021, 11, 10682 tangent, and real and imaginary parts of S21 into the training phase, the thickness classification models are established

Multilayer Structure Simulation

Results

Conclusions