Laboratory experiments are performed to understand the controlling parameters of the electrical field associated with the seepage of water through a porous material. We use seven glass bead packs with varying mean grain size in an effort to obtain a standard material for the investigation of these electrical potentials. The mean grain size of these samples is in the range 56–3000 μm. We use pure NaCl electrolytes with conductivity in the range 10−4 to 10−1 S m−1 at 25°C. The flow conditions cover viscous and inertial laminar flow conditions but not turbulent flow. In the relationship between the streaming potential coupling coefficient and the grain size, three distinct domains are defined by the values of two dimensionless numbers, the Dukhin and the Reynolds numbers. The Dukhin number represents the ratio between the surface conductivity of the grains (due to conduction in the electrical double layer coating the surface of the grains) and the pore water electrical conductivity. At high Dukhin numbers (≫1) and low Reynolds numbers (≪1), the magnitude of the streaming potential coupling coefficient decreases with the increase of the Dukhin number and depends on the mean grain diameter (and therefore permeability) of the medium. At low Dukhin and Reynolds numbers (≪1), the streaming potential coupling coefficient becomes independent of the microstructure and is given by the well-known Helmholtz-Smoluchowski equation widely used in the literature. At high Reynolds numbers, the magnitude of the streaming potential coupling coefficient decreases with the increase of the Reynolds number in agreement with a new model developed in this paper. A numerical application is made illustrating the relation between the self-potential signal and the intensity of seepage through a leakage in an embankment.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2006JB004673

Streaming potentials of granular media: Influence of the Dukhin and Reynolds numbers

Foreword Preface 1. Fundamentals of the self-potential method 2. Development of a fundamental theory 3. Laboratory investigations 4. Forward and inverse modeling 5. Applications to geohazards 6. Application to water resources 7. Application to hydrothermal systems 8. Seismoelectric coupling Appendix A: a simple model of the Stern layer Appendix B: the u-p formulation of poroelasticity References Index.

The Self-Potential Method: Theory and Applications in Environmental Geosciences

https://lup.lub.lu.se/search/ws/files/3871654/4934600.pdf

Resistivity monitoring for leakage and internal erosion detection at Hallby embankment dam

Crossline resistivity tomography was developed to find out anomalous seepage pathways in an embankment dam. By applying crossline tomography to the investigation of embankment dams, leakage pathways can be effectively located because the crossline tomogram presents resistivity distribution in the horizontal plane of an embankment dam. To test the effectiveness of crossline tomography, we applied it to data from an experiment designed to delineate anomalous seepage pathways in the embankment dam. The method yields relatively accurate geoelectric structure of the dam when applied to synthetic data. In the crossline resistivity tomogram, abrupt discontinuities of a low resistivity band corresponding to the core of the dam can be interpreted as leakage pathways. Application to real data obtained from an embankment dam in Korea yields the result which accurately depicts two anomalous seepage pathways. The identified pathways were consistent with low resistivity zones in the dipole-dipole resistivity section obtained on the crest of the dam. One pathway was confirmed by visual inspection of the dam, and afterward, by trenching.

Crossline resistivity tomography for the delineation of anomalous seepage pathways in an embankment dam

A finite element method (FEM) and an artificial neural network (ANN) model were developed to simulate flow through Jeziorsko earthfill dam in Poland. The developed FEM is capable of simulating two-dimensional unsteady and nonuniform flow through a nonhomogenous and anisotropic saturated and unsaturated porous body of an earthfill dam. For Jeziorsko dam, the FEM model had 5,497 triangular elements and 3,010 nodes, with the FEM network being made denser in the dam body and in the neighborhood of the drainage ditches. The ANN model developed for Jeziorsko dam was a feedforward three-layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the ANN model. The two models were calibrated and verified using the piezometer data collected on a section of the Jeziorsko dam. The water levels computed by the models satisfactorily compared with those measured by the piezometers. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM model. This case study offers insight into the adequacy of ANN as well as its competitiveness against FEM for predicting seepage through an earthfill dam body.

/pdf/case-study-finite-element-method-and-artificial-neural-ccx5ulxx3i.pdf

J.M. Shirke

Papers

Detection of seepage paths in earth dams using self-potential and electrical resistivity methods