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This article shows the possibility to adopt Support Vector Machine (SVM) learning method to predict moisture of building materials measured by the Time Domain Reflectometry (TDR) method. TDR is an indirect technique of moisture detection. It enables to evaluate apparent permittivity of moist material and then predict moisture using physical or empirical models. In this research it is presented the method that avoids evaluation of apparent permittivity value and estimate moisture basing on the raw TDR waveforms. SVM is one of the most popular machine learning methods that could be used both for classification and regression modelling. It is mostly applied for analysing of multidimensional signals, but could be also applied to evaluate moisture from raw TDR signals. SVM regression model allows quick estimation of material moisture and achieve similar or better measurement accuracy comparing to the standard calibration methods. Research was conducted on two types of building materials – the red and the silicate bricks and data analysis confirmed the suitability of SVM models in determining moisture content using the TDR method.
