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Electrical resistivity tomography to address earth dams and their failure at the laboratory scale
Abstract
Earth dams in Lebanon are typically used in remote agricultural areas where irrigation channels and large-scale reservoirs are not available. The use of non-invasive techniques for earth dam applications can be a significant step in the efficient monitoring of such structures, especially in Lebanon where there is substantial risk of earth dam failure. This work investigated the potential use of electrical resistivity tomographies (ERTs) in detecting dam seepage and failure using physical models at controlled laboratory conditions. It was shown that ERTs can indicate failure in such dams. Furthermore, a geostatistical investigation of these tomographies showed semivariogram analysis was successful in detecting failure and quantifying its spatial scale.
... Earth-rock-fill dams are more prone to experience failures. Take Laqlouq dam as an example, it failed in March 2015 due to heavy rain (Khoury 2016). Therefore, in order to maintain safe operation, dam monitoring is critical from initial construction phases throughout the expected life span of the structure (Ozaydin et al. 2006, Gikas and Sakellariou 2008, Kalkan 2014). ...
Dam is an important part of engineering structure, in the process of dam construction, the dam monitoring is crucial since water erosion and time-dependent motion may cause deformation. Traditional monitoring methods are time-consuming and labour-intensive. However, Interferometric Synthetic Aperture Radar (InSAR) can provide precise and spatially dense information on slow deformations. This research investigated the longest earth-rock-fill dam in Sudan to determine the spatial and temporal deformations Sentinel-1A descending SAR images were further used to analyse the issues mentioned above. The results suggested that the dam existed the maximum displacement with a value up to 190 mm on the dam crest. Besides, the selected sections along the riverbed of the dam were analysed and the RMSE was approximately 2 mm/year. The results were in good agreement with the in situ measurements, indicating the advancement of time series InSAR in dam deformation monitoring.
Electrical Resistivity Tomography (ERT) survey was carried out at Abu Baara earth dam in northwestern Syria, in order to delineate potential pathways of leakage occurring through the subsurface structure close to the dam body. The survey was performed along two straight measuring profiles of 715 and 430 m length in up-and downstream sides of the dam's embankment. The analysis of the inverted ERT sections revealed the presence of fractured and karstified limestone rocks which constitute the shallow bedrock of the dam reservoir. Several subsurface structural anomalies were identified within the fractured bedrock, most of which are associated with probable karstic cavities, voids, and discontinuity features developed within the carbonates rocks. Moreover, results also showed the occurrence of a distinguished subsiding structure coinciding with main valley course. Accordingly, it is believed that the bedrock and the other detected features are the main potential causes of water leakage from the dam's reservoir.
Resistivity measurements have been carried out in two embankment dams in order to develop non-destructive methods for seepage monitoring. The result indicates a seasonal resistivity variation, due to the seepage flow through the dam. This is mainly a result of the combined influence of the variation in temperature and concentration of total dissolved solids in the reservoir. Data of good quality were obtained, which is crucial for analyses of temporal variation. However, the 2-D data acquisition and interpretation technique used is a simplification of the 3-D reality. The seepage flow can be evaluated from the resistivity data using methods similar to those employed for seepage evaluation from temperature data. At seepage flows larger than around 10-6 m3/sm2,the resistivity variation inside the dam is mainly caused by the seasonal variations of the resistivity in the reservoir. Seepage flows evaluated from resistivity and temperature measurements show good agreement. Resistivity monitoring is non-destructive apart from the electrode installation and can provide measurements of time-dependent processes such as internal erosion.
Natural soils of various types have different electrical properties due to the composition, structure, water content, and temperature. In order to investigate the electrical properties of lateritic soil, electrical conductivity experiments have been conducted on a self-developed testing device. Test results show that the electrical conductivity of laterite increases with the increase of water content, degree of saturation and dry density. When the water content is below the optimum water content, the electrical conductivity of soils increases nonlinearly and the variation rate increases dramatically. However, when the water content, degree of saturation, or dry density increases to a certain value, the electrical conductivity tends to be a constant. In addition, soil electrical conductivity increases with the increase of temperature, and it is observed that the electrical conductivity decreases with the increase of the number of wetting–drying cycles.
To determine and map the subsurface conditions of a dam, a 2D electrical resistivity tomography study was carried out within the two flanks of Zaria dam at Shika. This was done to ascertain if the variations in the volume of water content in the dam is due to an anomalous seepage beneath the subsurface or seasonal effects. On the basis of the interpretation of the acquired data, various zones of relatively uniform resistivity values were mapped and identified. The first zone is characterized by moderate resistivity values of 150 - 600 ohm-m. It represents unsaturated topsoil with thicknesses varying from 1 - 4.5 m. The second (intermediate depth) resistivity zone, with values ranging from 5 - 100 ohm-m and thickness varying from 3.5 - 10 m, represents a silt clay layer with high moisture content. The third resistivity zone represents fairly weathered granite and is characterized by relatively high resistivity values ranging from 700 - 6000 ohm-m. The available borehole log data correlated well with the pseudo-sections in relation to the obtained resistivity values and depth. Zones of relatively low resistivity within the bedrock are interpreted to represent potential seepage pathways. Hence, this geophysical method can be successfully used to delineate and map these seepage pathways within the subsurface of the earth dam.
Soil surveys and the information they provide are commonly believed to be good investment, with significant benefits accrued to their users. To date, the empirical evidence for this comes from studies that have shown how enhanced soil information can alter agricultural practices to yield higher returns. This study attempts to estimate the economic value of soil information generated by a host of new proximal and in situ geophysical methods for the assessment of the following soil properties: carbon content, water content, clay content, bulk density, and soil depth. The study also adopts a novel approach to the economic valuation of soil information by employing for the first time a choice experiment to estimate the willingness-to-pay (WTP) of potential users of the digital maps and their features
Work carried out on the electrical resistivity characteristics of a clay-water electrolyte system is described. The samples were tested over a range of consistency indices and at varying degrees of compaction. Resistivity/fractional volume of water relationships have been established for two different clays over a practical range of moisture contents, and a unique relationship exists between these parameters for each day. The resistivity of a clay is a function of moisture content and degree of saturation. At a particular moisture content, an increase in the degree of saturation (or a decrease in the air-void ratio) will mean a decrease in resistivity.
Quick clay has been involved in most serious, large clay slides in Sweden, Norway, and Canada. This paper describes geotechnical
and geophysical methods that can be used to locate and map the extent of quick clay formations. Surface resistivity measurements
and four different sounding methods have been tested. The results have been compared with sensitivities determined using fall-cone
tests. The investigation shows that there is a correlation between sensitivity and electrical resistivity, which can be used
to discriminate between marine clays that have been leached sufficiently to possibly form quick clay and those for which the
salt content remains sufficiently high to prevent this. Although the most reliable evaluation of the variation in sensitivity
was obtained by the CPT with additional measurement of total penetration force, this investigation suggests that any sounding
method that uses a constant rate of advance into the ground and in which the penetration force applied on the top of the rods
is measured may be used for quick clay mapping.
Objects in the terrestrial environment interact differentially with electromagnetic radiation according to their essential physical, chemical and biological properties. This differential interaction is manifest as variability in scattered radiation according to wavelength, location, time, geometries of illumination and observation and polarization. If the population of scattered radiation could be measured, then estimation of these essential properties would be straightforward. The only problem would be linking such estimates to environmental variables of interest. This review paper is divided into three parts. Part 1 is an overview of the attempts that have been made to sample the five domains of scattered radiation (spectral, spatial, temporal, geometrical, polarization) and then to use the results of this sampling to estimate environmental variables of interest. Part one highlights three issues: first, that relationships between remotely sensed data and environmental variables of interest are indirect; second, our ability to estimate these environmental variables is dependent upon our ability to capture a sound representation of variability in scattered radiation and third, a considerable portion of the useful information in remotely sensed images resides in the spatial domain (within the relations between the pixels in the image). This final point is developed in Part 2 that explores ways in which the spatial domain is utilized to describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data and to increase the accuracy with which remotely sensed data can be used to estimate both discontinuous and continuous variables. Part 3 outlines two specific uses of information in the spatial domain; first, to select an optimum spatial resolution and second, to inform an image classification.
The development of direct current resistivity imaging techniques has been rapid in the last years. This applies to data acquisition as well as inverse modelling techniques, and has lead to a greatly expanded practical applicability of the method. Resistivity imaging is now becoming widely used in environmental and engineering applications where increased knowledge about the subsurface is sought. The ongoing development can be expected to continue.
This work aims to be an initial investigation to the use of geophysical and limited geotechnical data to obtain soil and stratigraphy characterization. This approach is essential for developing countries where geotechnical data is typically scarce and expensive. This type of consolidation was used on a study area in Lebanon that consists of a failed slope. A consolidation of data from surface wave measurements, electrical resistivity, and in-situ data was carried out where each gave a part of the description of the site. The surface wave measurements revealed that the seismic bedrock depth was not reached in the range of measurements carried out for this study. Soil was revealed to be mostly clayey sand. Geostatistical analysis results showed that spatial variability of soil properties in both vertical and horizontal directions could be achieved from the electrical resistivity measurements. For this study area, it was shown that 9 m and 4 m were the sizes of the essential spatial structures in the data in the horizontal and vertical directions respectively. Another finding of interest was a second order trend in the resistivity data in both the horizontal and vertical directions that can be due to the decrease in resistivity with depth due to soil compression. All in all, the slope has been better understood with the merging of data and the aspects that require further investigation have been clearly depicted.
The concept of spatial scale is fundamental to geography, as are the problems of integrating data obtained at different scales. The availability of GIS has provided an appropriate environment to re-scale data prior to subsequent integration, but few tools with which to implement the re-scaling. This sparsity of appropriate tools arises primarily because the nature of the spatial variation of interest is often poorly understood and, specifically, the patterns of spatial dependence and error are unknown. Spatial dependence can be represented and modelled using geostatistical approaches providing a basis for the subsequent re-scaling of spatial data (e.g., via spatial interpolation). Geostatistical techniques can also be used to model the effects of re-scaling data through the geostatistical operation of regularization. Regularization provides a means by which to re-scale the statistics and functions that describe the data rather than the data themselves. These topics are reviewed in this paper and the importance of the spatial scale problems that remain is emphasized.
The aim of this study is to set a foundation for mapping Beirut soils using available soil reports obtained from excavations. With the availability of a certain number of excavations and soil reports, one could attempt to gather soil data in order to understand the subbase and map the stratigraphy of the city. However, only a limited number of boreholes and soil data are available. Consequently, the use of innovative methods to analyze and assess limited numbers of heterogeneous data is necessary for developing countries where data is generally scarce. The paper investigates the available soil report data, specifically soil cohesion (c), soil friction angle (φ), and soil type classification. The results show that soil cohesion and the soil friction angle of Beirut have a high negative correlation r2 = -0.71. Then, a mixture of Gaussians (MG) clustering approach is utilized to find meaningful patterns in the data, and thus resulting in three categories according to which the soil is to be mapped in future work. The categories are related to ranges in values of c and φ and their corresponding soil types.
In geostatistics, spatial autocorrelation is utilized to estimate optimally local values from data sampled elsewhere. The powerful synergy between geostatistics and remote sensing went unrealized until the 1980s. Today geostatistics are used to explore and describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data; and to increase the accuracy with which remotely sensed data can be used to classify land cover or estimate continuous variables. This article introduces these applications and uses two examples to highlight characteristics that are common to them all. The article concludes with a discussion of conditional simulation as a novel geostatistical technique for use in remote sensing.
The semi‐variogram is central to geostatistics and the single most important tool in geo‐statistical applications to soil. Mathematical functions for semi‐variograms must be conditional negative semi‐definite, and there are only a few families of simple function that meet this demand. These include the transitive models with finite a priori variance deriving from moving average processes. The spherical and exponential schemes are the most often encountered members. The other major group is that of unbounded models in which the variance appears to increase without limit. The linear model is the most common in this group. If more complex models are needed they can be formed by combining two or more simple models.
The usual estimator of the semi‐variance is often considered inefficient and to be sensitive to departures from normality in the data. It is compared with a robust estimator and shown to be generally preferable in being unbiased and having confidence intervals that are no wider. For routine analysis, fitting models to sample semi‐variograms by weighted least squares approximation, with weights proportional to the expected semi‐variance, is preferred to the more elaborate and computationally demanding statistical procedures of generalized least squares and maximum likelihood. The Akaike information criterion is recommended for selecting the best model from several plausible ones to describe the observed variation in soil, though for kriging it may be desirable to validate the chosen model.
Examples of models fitted to soil semi‐variograms are shown and compared.
The usefulness of the electrical resistivity log in determining reservoir characteristics is governed largely by: (i) the accuracy with which the true resistivity of the formation can be determined; (2) the scope of detailed data concerning the relation of resistivity measurements to formation characteristics; (3) the available information concerning the conductivity of connate or formation waters; (4) the extent of geologic knowledge regarding probable changes in facies within given horizons, both vertically and laterally, particularly in relation to the resultant effect on the electrical properties of the reservoir. Simple examples are given in the following pages to illustrate the use of resistivity logs in the solution of some problems dealing with oil and gas reservoirs. From the available information, it is apparent that much care must be exercised in applying to more complicated cases the methods suggested. It should be remembered that the equations given are not precise and represent only approximate relationships. It is believed, however, that under favorable conditions their application falls within useful limits of accuracy.
The concept of spatial scale is fundamental to geography, as are the problems of integrating data obtained at different scales. The availability of GIS has provided an appropriate environment to re-scale data prior to subsequent integration, but few tools with which to implement the re-scaling. This sparsity of appropriate tools arises primarily because the nature of the spatial variation of interest is often poorly understood and, specifically, the patterns of spatial dependence and error are unknown. Spatial dependence can be represented and modelled using geostatistical approaches providing a basis for the subsequent re-scaling of spatial data (e.g., via spatial interpolation). Geostatistical techniques can also be used to model the effects of re-scaling data through the geostatistical operation of regularization. Regularization provides a means by which to re-scale the statistics and functions that describe the data rather than the data themselves. These topics are reviewed in this paper and the importance of the spatial scale problems that remain is emphasized.
Why geostatistics?A little historyFinding your way
Geostatistics is essential for environmental scientists. Weather and climate vary from place to place, soil varies at every scale at which it is examined, and even man-made attributes - such as the distribution of pollution - vary. The techniques used in geostatistics are ideally suited to the needs of environmental scientists, who use them to make the best of sparse data for prediction, and top plan future surveys when resources are limited. Geostatistical technology has advanced much in the last few years and many of these developments are being incorporated into the practitioner's repertoire. This second edition describes these techniques for environmental scientists. Topics such as stochastic simulation, sampling, data screening, spatial covariances, the variogram and its modeling, and spatial prediction by kriging are described in rich detail. At each stage the underlying theory is fully explained, and the rationale behind the choices given, allowing the reader to appreciate the assumptions and constraints involved.
Multivariate Geostatistics
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