Using machine learning algorithms to link volumetric water content to complex dielectric permittivity in a wide (33–2000 MHz) frequency band for hydraulic concretes

Abstract

ABSTRACTThis paper focuses on the development and validation of an innovative method for estimating volumetric water content in concrete mixtures. A supervised learning method (support vector machine) has been used to resolve the inverse problem, i.e., generate in‐laboratory calibration curves correlating the controlled water content in various concrete mixtures with the frequency‐dependent complex dielectric permittivity originating from the coaxial electromagnetic transition line. An extrapolation procedure using a frequency‐power‐law model has been developed and validated for estimating the complex permittivity over a broad frequency bandwidth. Implementation of this extrapolation method allows considering various physical phenomena (i.e., polarisation versus water content) that typically affect the dielectric behaviour of concrete as a function of frequency. The two‐step estimation procedure (involving extrapolation and support vector regression methods) proposed in this paper has been validated on a wide array of moisture‐controlled concrete specimens in the laboratory. The procedure helps building calibration curves that rely on both complex effective permittivity and volumetric water content, taking into consideration the frequency dependence.