A method for the simultaneous determination of the soil dry density and moisture content based on thermal effects

Abstract

The thermodynamic parameters of soil are affected by both dry density and moisture content, leading to uncertainty in measuring moisture content using the heat source method. This study proposes a combined approach using a back propagation (BP) neural network and the point heat resource method to simultaneously determine soil dry density and moisture content. The segmented mean value extracted from the temperature time-history data during the cooling process of the heat source serves as the feature input, while measured values of dry density and moisture content serve as outputs. A calibrated BP neural network model is trained and utilized for simultaneous determination of both parameters. Numerical simulations and modeling tests demonstrate good agreement between inverse identification results and measurements, with root mean square errors of 1.65% for moisture content and 34.09 kg∙m−3 for dry density, along with coefficients of determination at 0.9482 and 0.9359 respectively. It is proved that the method combining soil thermal effect and BP neural network to measure soil dry density and moisture content is feasible.