Deep Learning for Exploring the Relationship Between Geotechnical Properties and Electrical Resistivities

Abstract

Electrical resistivity imaging is gaining popularity in aiding the characterization of subsurface conditions and assessment of the stability of earth materials. Nevertheless, it remains challenging to identify the relationship between geotechnical properties and electrical resistivities because of their nonlinear and complex relationship. This study intends to assess the application of the deep learning model to explore the relationship between electrical resistivities and geotechnical properties of natural clayey soils. A full factorial design was used to study the effects of water content and dry unit weight on the electrical resistivities of soils composed of different fractions of fine and clay particles. A deep learning model with three hidden layers was trained using a dataset comprising 842 observations to investigate the association between electrical resistivities and geotechnical properties. Influencing geotechnical properties were identified by Spearman’s correlation and feature importance. The results show that most variabilities in the electrical resistivity can be explained by the water content and dry unit weight. The results also show that the plasticity index and fine fraction play a more substantial role in predicting the electrical resistivities of clayey soils than the liquid limit and clay fraction. A comparison between the accuracy metrics of the deep learning model with existing models in the literature shows that deep learning outperforms other models in discovering nonlinear and complex relationships between electrical resistivities and geotechnical properties. Enhanced knowledge of the relationship between geotechnical properties and electrical resistivities allows for better characterizing the subsurface conditions to improve reliability and reduce uncertainties caused by inadequate subsurface information.