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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Civil Engineering

Background:

  • Accurate concrete characterization is crucial for structural integrity.
  • Electromagnetic wave methods are used for evaluating concrete properties.
  • Complex dielectric permittivity (ϵr(f)) is sensitive to physical parameters affecting mechanical performance.

Purpose of the Study:

  • To investigate Gaussian Process Regression (GPR) for modeling concrete's complex dielectric permittivity (ϵr(f)).
  • To compare GPR's performance against the established Jonscher model.
  • To assess the reliability and accuracy of GPR in characterizing ϵr(f) across frequencies.

Main Methods:

  • Utilized Gaussian Process Regression (GPR), a data-driven technique.
  • Employed the universal Jonscher model as a benchmark for comparison.
  • Evaluated model performance using accuracy metrics and confidence intervals for predictions.

Main Results:

  • GPR demonstrated superior accuracy in characterizing concrete's complex dielectric permittivity compared to the Jonscher model.
  • GPR provided reliable predictions with associated confidence intervals, indicating estimate reliability across frequencies.
  • The frequency-dependent behavior of ϵr(f) was effectively modeled by GPR.

Conclusions:

  • Gaussian Process Regression is a highly effective data-driven method for modeling concrete's complex dielectric permittivity.
  • GPR offers improved accuracy and reliability over the Jonscher model for ϵr(f) characterization.
  • Accurate permittivity modeling aids in informed decisions for building reconstruction and repair.