Non-Destructive Intelligent Microwave Sensing of Water Content in Sand–Water Mixtures Using S-Parameter Features and PLS Regression

The accurate and rapid estimation of water content in granular materials is important for geotechnical engineering, construction-material evaluation, agricultural monitoring, and non-destructive material assessment. This study presents a controlled laboratory feasibility framework for estimating water content in sand–water mixtures using microwave S-parameter measurements and physically interpretable features. Sixty measurements were acquired from six nominal mixture levels containing 0%, 5%, 10%, 15%, 20%, and 25% water by mass using a WR-229 waveguide-based fixture over 3.30–4.90 GHz. Descriptors were extracted from the raw and empty-reference-normalized S11 and S21 responses, including extrema, slopes, area-based indicators, band-averaged values, selected-frequency responses, and phase statistics. Ridge regression, partial least squares regression, support vector regression, Gaussian process regression, random forest, and gradient boosting regression were evaluated. The selected PLS model achieved RMSE = 3.56%, MAE = 2.92%, and R2 = 0.826 under leave-one-mixture-level-out group-wise cross-validation, which was used as the primary indicator of generalization to an unseen mixture level. The substantially lower error obtained under repeated-measurement leave-one-out cross-validation primarily reflects within-level repeatability under controlled conditions and should not be interpreted as evidence of universal calibration performance. The results demonstrate that magnitude- and phase-derived microwave descriptors, particularly transmission-based features, provide an interpretable and repeatable framework for water-content estimation under the specific sand type, sample geometry, water source, and laboratory conditions investigated. Further validation using independent preparation batches, different granular materials, measured water conductivity, temperature variation, and field-like conditions is required before practical deployment.