Electromagnetic Characterization of Concrete by Jonscher Universal Dielectric Model: Numerical Validation of Different Nonlinear Optimization Algorithms

The dielectric permittivity model of concrete plays a key role in characterizing its interaction with electromagnetic (EM) waves but also in determining its mechanical properties. Among various EM models, the Jonscher universal dielectric response has been favored by scientists and practitioners in...

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Bibliographic Details
Published in:IEEE access Vol. 13; pp. 142635 - 142650
Main Authors: Angiulli, Giovanni, Versaci, Mario
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation algorithms
complex dielectric permittivity
Concrete
Convergence
Dielectrics
Electromagnetic properties
Electromagnetics
Jonscher model
Least squares method
Mathematical models
Mechanical properties
Numerical methods
Numerical models
Optimization
Optimization algorithms
optimization-based methods
Permittivity
Solid modeling
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:The dielectric permittivity model of concrete plays a key role in characterizing its interaction with electromagnetic (EM) waves but also in determining its mechanical properties. Among various EM models, the Jonscher universal dielectric response has been favored by scientists and practitioners in the field for the above purposes. Despite its widespread use, to the best of our knowledge, no study has focused its attention on the problem of choosing the most appropriate numerical method and the related starting point solution to solve the nonlinear least squares problem for fitting the Jonscher model to the experimental data. In this work, considering their broad adoption within software packages such as R, Python, or MATLAB, the performances of the Nelder-Mead, the Broyden-Fletcher-Goldfarb-Shanno, and the Levenberg-Marquardt nonlinear optimization algorithms have been compared among them to obtain practical guidance on which nonlinear optimization procedure to choose and how to generate a valid initial guess solution to achieve optimal fitting with the experimental data.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3598642