Optimising the complex refractive index model for estimating the permittivity of heterogeneous concrete models

Hossain Zadhoush*, Antonios Giannopoulos, Iraklis Giannakis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

Estimating the permittivity of heterogeneous mixtures based on the permittivity of their components is of high importance with many applications in ground penetrating radar (GPR) and in electrodynamics-based sensing in general. Complex Refractive Index Model (CRIM) is the most mainstream approach for estimating the bulk permittivity of heterogeneous materials and has been widely applied for GPR applications. The popularity of CRIM is primarily based on its simplicity while its accuracy has never been rigorously tested. In the current study, an optimised shape factor is derived that is fine-tuned for modelling the dielectric properties of concrete. The bulk permittivity of concrete is expressed with respect to its components i.e., aggregate particles, cement particles, air-voids and volumetric water fraction. Different combinations of the above materials are accurately modelled using the Finite-Difference Time-Domain (FDTD) method. The numerically estimated bulk permittivity is then used to fine-tune the shape factor of the CRIM model. Then, using laboratory measurements it is shown that the revised CRIM model over-performs the default shape factor and provides with more accurate estimations of the bulk permittivity of concrete.

Original languageEnglish
Article number723
Number of pages15
JournalRemote Sensing
Volume13
Issue number4
DOIs
Publication statusPublished - 16 Feb 2021

Keywords

  • Antenna
  • Concrete
  • CRIM model
  • FDTD
  • GPR
  • GprMax
  • GPU
  • NDT
  • Permittivity measurement
  • Shape factor
  • Time-zero

Fingerprint

Dive into the research topics of 'Optimising the complex refractive index model for estimating the permittivity of heterogeneous concrete models'. Together they form a unique fingerprint.

Cite this