Using machine learning algorithms to link volumetric water content to complex dielectric permittivity in a wide (33–2000 MHz) frequency band for hydraulic concretes
Authors:
A. Ihamouten, C. Le Bastard, X. Dérobert, F. Bosc and G. Villain
Issue: Vol 14, No 6, December 2016 pp. 527 - 536
DOI: 10.3997/1873-0604.2016045
Language: English
Info: Article, 
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Abstract:
This 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.
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