Estimation of bulk electrical conductivity in saline medium with contaminated lead solution through TDR coupled with machine learning

Time-domain reflectometry (TDR) has been used for the characterization of media; however, the results of TDR tests significantly differ according to the types of solutions. The objective of this study is to suggest a new relationship between TDR output values and bulk electrical conductivity based on a machine learning algorithm for enhancing the reliability of TDR measurement. Various salinities (0%, 1%, 2%, and 3%) and lead concentrations (0, 0.5, 1, 2, 5, and 10 mg/L) are applied along with silica sand, classified as SP in USCS, to create media. A laboratory test is performed to measure the TDR waveform at the bottom of the cylindrical cell, and a resistance probe is also installed to obtain the true bulk electrical conductivity in the cell. A deep neural network machine learning algorithm is applied to establish the relationship between the TDR output value and the bulk electrical conductivity at each frequency of 0.1, 0.12, 1, 10, and 100 kHz. The highly important variables are also defined through random forest. This study demonstrates that the TDR can be reliably converted into bulk electrical conductivity when two different solutions are mixed.