Identification of Hydraulic Conductivity Structure in Sand and Gravel Aquifers: Cape Cod Data Set

AbstractA data set of cemented sand and gravel (CSG) mix proportion and 28-day compressive strength was established, with outliers determined and removed based on the Boxplot. Then, the distribution law of compressive strength of CSG was analyzed using the skewness kurtosis and single-sample Kolmogorov-Smirnov tests. And with the help of Python software, a model based on Back Propagation neural network was built to predict the compressive strength of CSG according to its mix proportion. The results showed that the compressive strength follows the normal distribution law, the expected value and variance were 5.471 MPa and 3.962 MPa respectively, and the average relative error was 7.16%, indicating the predictability of compressive strength of CSG and its correlation with the mix proportion.

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Predicting the saturated hydraulic conductivity of sand and gravel using effective diameter and void ratio

Canadian Geotechnical Journal ◽

10.1139/t04-022 ◽

2004 ◽

Vol 41 (5) ◽

pp. 787-795 ◽

Cited By ~ 182

Author(s):

Robert P Chapuis

Keyword(s):

Hydraulic Conductivity ◽

Void Ratio ◽

Saturated Hydraulic Conductivity ◽

Effective Diameter ◽

Predictive Capacity ◽

K Value ◽

Maximum Value ◽

New Equation ◽

Silty Soils ◽

Sand And Gravel

This paper assesses methods to predict the saturated hydraulic conductivity, k, of clean sand and gravel. Currently, in engineering, the most widely used predictive methods are those of Hazen and the Naval Facilities Engineering Command (NAVFAC). This paper shows how the Hazen equation, which is valid only for loose packing when the porosity, n, is close to its maximum value, can be extended to any value of n the soil can take when its maximum value of n is known. The resulting extended Hazen equation is compared with the single equation that summarizes the NAVFAC chart. The predictive capacity of the two equations is assessed using published laboratory data for homogenized sand and gravel specimens, with an effective diameter d10 between 0.13 and 1.98 mm and a void ratio e between 0.4 and 1.5. A new equation is proposed, based on a best fit equation in a graph of the logarithm of measured k versus the logarithm of d102e3/(1 + e). The distribution curves of the differences log(measured k) log(predicted k) have mean values of 0.07, 0.21, and 0.00 for the extended Hazen, NAVFAC, and new equations, respectively, with standard deviations of 0.23, 0.36, and 0.10, respectively. Using the values of d10 and e, the new equation predicts a k value usually between 0.5 and 2.0 times the measured k value for the considered data. It is shown that the predictive capacity of this new equation may be extended to natural nonplastic silty soils, but not to crushed soils or plastic silty soils. The paper discusses several factors affecting the inaccuracy of predictions and laboratory test results.Key words: permeability, sand, prediction, porosity, gradation curve.

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Sewage plume in a sand and gravel aquifer, Cape Cod, Massachusetts

Open-File Report ◽

10.3133/ofr82274 ◽

1982 ◽

Cited By ~ 3

Author(s):

D.R. LeBlanc

Keyword(s):

Cape Cod ◽

Sand And Gravel ◽

Gravel Aquifer

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Infiltration and Solute Transport Experiments in Unsaturated Sand and Gravel, Cape Cod, Massachusetts: Experimental Design and Overview of Results

Water Resources Research ◽

10.1029/95wr02972 ◽

1996 ◽

Vol 32 (3) ◽

pp. 519-532 ◽

Cited By ~ 20

Author(s):

David L. Rudolph ◽

R. Gary Kachanoski ◽

Michael A. Celia ◽

Denis R. LeBlanc ◽

Jonathon H. Stevens

Keyword(s):

Experimental Design ◽

Solute Transport ◽

Cape Cod ◽

Unsaturated Sand ◽

Sand And Gravel

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Global Mapping of Soil Saturated Hydraulic Conductivity Combining Legacy Data, Spatial Covariates and Machine Learning

10.5194/ismc2021-90 ◽

2021 ◽

Author(s):

Surya Gupta ◽

Peter Lehmann ◽

Andreas Papritz ◽

Tomislav Hengl ◽

Sara Bonetti ◽

...

Keyword(s):

Machine Learning ◽

Hydraulic Conductivity ◽

Cross Validation ◽

Arable Land ◽

Remotely Sensed ◽

Saturated Hydraulic Conductivity ◽

Pedotransfer Functions ◽

Data Set ◽

Global Database ◽

Spatially Distributed

<p>Saturated soil hydraulic conductivity (Ksat) is a key parameter in many hydrological and climatic modeling applications, as it controls the partitioning between precipitation, infiltration and runoff. Values of Ksat are often deduced from Pedotransfer Functions (PTFs) using maps of soil attributes. To circumvent inherent limitations of present PTFs (heavy reliance of arable land measurements, ignoring soil structure, and geographic bias to temperate regions), we propose a new global Ksat map at 1&#8211;km resolution by harnessing technological advances in machine learning and availability of remotely sensed surrogate information (terrain, climate and vegetation). We compiled a comprehensive Ksat data set with 13,258 data geo-referenced points from literature and other sources. The data were standardized and quality-checked in order to provide a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB was then applied to develop a Covariate-based GeoTransfer Function (CoGTF) model for predicting spatially distributed Ksat values using remotely sensed information on various environmental covariates. The model accuracy assessment based on spatial cross-validation shows a concordance correlation coefficient (CCC) of 0.16 and a root meansquare error (RMSE) of 1.18 for log10 Ksat values in cm/day (CCC=0.79 and RMSE=0.72 for non spatial cross-validation). The generated maps of Ksat represent spatial patterns of soil formation processes more distinctly than previous global maps of Ksat based on soil texture information and bulk density. The validation indicates that Ksat could be modeled without bias using CoGTFs that harness spatially distributed surface and climate attributes, compared to soil information based PTFs. The relatively poor performance of all models in the validation (low CCC and high RMSE) highlights the need for the collection of additional Ksat values to train the model for regions with sparse data.</p>

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Travel-time-based thermal tracer tomography

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1885-2016 ◽

2016 ◽

Vol 20 (5) ◽

pp. 1885-1901 ◽

Cited By ~ 21

Author(s):

Márk Somogyvári ◽

Peter Bayer ◽

Ralf Brauchler

Keyword(s):

Hydraulic Conductivity ◽

Grid Method ◽

Early Time ◽

Thermal Conditions ◽

Breakthrough Curves ◽

Injection Rate ◽

Staggered Grid ◽

Travel Times ◽

Data Set ◽

Technical Parameters

Abstract. Active thermal tracer testing is a technique to get information about the flow and transport properties of an aquifer. In this paper we propose an innovative methodology using active thermal tracers in a tomographic setup to reconstruct cross-well hydraulic conductivity profiles. This is facilitated by assuming that the propagation of the injected thermal tracer is mainly controlled by advection. To reduce the effects of density and viscosity changes and thermal diffusion, early-time diagnostics are used and specific travel times of the tracer breakthrough curves are extracted. These travel times are inverted with an eikonal solver using the staggered grid method to reduce constraints from the pre-defined grid geometry and to improve the resolution. Finally, non-reliable pixels are removed from the derived hydraulic conductivity tomograms. The method is applied to successfully reconstruct cross-well profiles as well as a 3-D block of a high-resolution fluvio-aeolian aquifer analog data set. Sensitivity analysis reveals a negligible role of the injection temperature, but more attention has to be drawn to other technical parameters such as the injection rate. This is investigated in more detail through model-based testing using diverse hydraulic and thermal conditions in order to delineate the feasible range of applications for the new tomographic approach.

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