期刊
NEAR SURFACE GEOPHYSICS
卷 14, 期 6, 页码 527-536出版社
EUROPEAN ASSOC GEOSCIENTISTS & ENGINEERS
DOI: 10.3997/1873-0604.2016045
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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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