In viewpoint of geotechnical engineering, the prediction of anomaly presence in subsurface and the estimation of its position, size and state play an important role in designing structural foundations and characterizing their overall mechanical behaviors. The present study develops an enhanced algorithm for detecting anomalies in particulate materials effectively using an electrical resistivity survey. The algorithm was analytically derived using Gauss' law and Laplace's equation. A series of experimental tests was performed on anomalies that were different in terms of size, location, and type in order to verify the developed algorithm. The location, size, and characteristics of the anomalies in particulate materials are predicted from measured resistances through proper inversion processing. A comparison of the predicted and measured values shows that anomalies can be detected effectively using the electrical resistivity-based enhanced algorithm developed in this study.
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Condensed Matter Physics