A Data-Driven Gaussian Process Regression Model for Concrete Complex Dielectric Permittivity Characterization

Concrete diagnosis is an important task in making informed decisions about reconstructing or repairing buildings. Among the different approaches for evaluating its characteristics, methods based on electromagnetic waves have been proposed in the literature over the years. In this context, the characterization of concrete complex dielectric permittivity ϵr(f) (where <i>f</i> is the frequency) has received considerable attention, taking into account that its values and its frequency behavior are both sensitive to a series of physical parameters, which in turn can significantly influence the mechanical performance of concrete. Recently, data-driven techniques have emerged as alternatives for modeling material properties due to their regression and generalization potential. Following this research line in this work, we investigated the potential of Gaussian Process Regression to model ϵr(f) by comparing its performance with that of the model most employed to characterize the concrete dielectric permittivity: the universal Jonscher model. The inherent ability to provide predictions accompanied by confidence intervals, which allows the assessment of the reliability of the permittivity estimate across frequency, and the related error metrics demonstrate that GPR can effectively characterize ϵr(f) in an effective manner, outperforming the Jonscher model in terms of accuracy in all the cases considered in our study.