Review of Correlations Between Soil Electrical Resistivity and Geotechnical Properties

Abstract

This review presents a comprehensive synthesis of existing studies on the correlations between soil electrical resistivity and fundamental geotechnical properties, with the aim of improving the applicability of resistivity testing in geotechnical engineering. Based on both laboratory and field research, the analysis highlights moisture content as the most influential factor inversely affecting resistivity, followed by plasticity index, clay content, and dry unit weight. Statistical approaches—including regression analysis and Spearman’s rank correlation—and artificial neural networks (ANNs) were evaluated for their effectiveness in modeling these nonlinear relationships. ANNs demonstrated superior predictive performance, particularly in heterogeneous and fine-grained soils. The review also examines the influence of temperature and salinity, emphasizing the need for multivariable models for robust interpretation. While resistivity testing shows great potential for non-destructive soil classification and compaction assessment, limitations related to soil type specificity, environmental variability, and model generalizability are noted. This work provides an important foundation for the development of data-driven, non-invasive techniques for subsurface characterization and outlines directions for future research, including field validation and hybrid modeling.

Keywords

• artificial neural networks (ANNs)
• correlation
• geotechnical properties
• regression analysis
• soil electrical resistivity