Hydraulic Conductivity of Soils from Grain-Size Distribution: New Models

Boadu, Fred Kofi

doi:10.1061/(asce)1090-0241(2000)126:8(739)

Cited by 124 publications

(58 citation statements)

References 17 publications

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“…As regards the Environment Diagnosis, several methods have been developed in order to use textural analysis to deduce depositional environment and these methods are chiefly empirical in nature, utilizing statistical parameters based upon grain-size frequency distributions (Davis, 1973;Boadu, 2000;Boggs, 2005;Odong, 2007). They fall into these general categories of analysis namely: a.…”

Section: Textural (Grain Size) Analysismentioning

confidence: 99%

The use of statistical grain-size method in analysing borehole and evaluating aquifer parameters. A case study of Ajali Sandstone formation, southeastern Nigeria

Ezim¹,

Obiadi²,

Akaegbobi³

2017

Glo Jnl Geo Sci

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Several challenges abound with the accurate estimation of aquifer parameters in Groundwater resource development. This paper describes the investigation on the aquifer (mainly Ajali Sandstone Formation) parameters in Nsukka area. The Ajali Sandstone Formation is among the principal sources of groundwater in the area. (9) boreholes drilled in the area were analysed texturally using statistical grain size method.Results show that the highly permeable members of Ajali Sandstone are devoid of surface waters and are seen to be friable, medium-grained, poorly sorted, and highly cross-bedded. Recharge is mainly through rainfall. The distribution of major geological units, well log data, static water level data, and surface features were found to have influenced groundwater occurrence and flow pattern in the study area. The lithological logs showed sandy aquifers separated by clayey aquitards, while stratigraphic analyses of well lithological logs revealed the presence of at least four layers of contrasting hydraulic conductivity, making the aquiferous Ajali Sandstone homogenous and anisotropic. Values of permeability (k) from 0.00753 to 0.01512 cm 2 , hydraulic conductivity (K) from 8.2886 to 16.6444 m/s, transmissivity (T) between 455.87 and 1002.48m 2 s -1 , and specific yield from 2.35 to 4.76m 3 were found to be consistently high, thus suggesting thick and prolific aquiferous zones.

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Section: Textural (Grain Size) Analysismentioning

confidence: 99%

The use of statistical grain-size method in analysing borehole and evaluating aquifer parameters. A case study of Ajali Sandstone formation, southeastern Nigeria

Ezim¹,

Obiadi²,

Akaegbobi³

2017

Glo Jnl Geo Sci

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“…To understand why the USCS is inadequate, consider that two soil samples from one site may meet the grain-size requirements of a single USCS class but at the same time have very different densities, moisture contents, and saturated permeabilities. Because moisture content, density, and saturated permeability are known to affect sensor response (Andraski 1996;Boadu 2000;Curtis 2001;Koh and Wakeley 2010), the two soils that would be considered a single material in the USCS may impact a given sensor modality in very different ways. Thus, the method used to define soils materials for GEOTACS modeling must be based on properties that are expected to impact sensor response rather than on soil classes alone.…”

Section: The Need To Define Materials Types For 3-d Soil Modelingmentioning

confidence: 99%

Defining Soil Materials for 3-D Models of the Near Surface: Preliminary Findings

Roig-Silva¹,

Haugen²,

Wakeley³

2012

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In support of three-dimensional modeling of soils in the near surface, a method was developed to define soil material types quantitatively using statistical comparisons of properties expected to impact sensor performance. To maintain technical continuity with other soils research and ensure relevance to soils engineering, many of the parameters chosen for statistical comparisons included conventional properties familiar to geotechnical researchers. Other, more sensor-specific soil properties, such as effective saturation and thermal conductivity, were used to allow for direct correlation between classified soil materials and sensor response. Initial trials of the method evaluated datasets from four sites in the U.S. and Asia. Early results showed that the number of statistically distinguishable materials tends to mimic the number of soil horizons sampled for a given dataset. Results also revealed a critical need for further research on the interactions of various soil properties and states between and among each other to determine the combinations that have the strongest influence on sensor response patterns. This protocol for delineating near-surface soil materials advances earlier techniques and improves the state-of-knowledge of modeling geologic features in three dimensions for sensor simulation. DISCLAIMER:The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

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“…Chen et al [26] found that the hydraulic conductivity is strongly related to the median diameter D 50 . Boadu [27] proposed the multivariate regression model to overcome the difficulty in the previous models. However, if soils are artificially mixed and compacted with selected particle sizes, those conductivity models for natural soils composed with various particle sizes are limited.…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Conductivity Variation of Coarse‐Fine Soil Mixture upon Mixing Ratio

Chung

Kim

et al. 2018

Advances in Civil Engineering

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is paper presents the theoretical and experimental investigations of the hydraulic conductivity variation of the soil mixture that contains two distinct particle size distributions, coarse and fine soils. A new model for the hydraulic conductivity is introduced that focuses on the relationship between the coarse-fine soil mixing ratio and the hydraulic conductivity of the mixture. For the model verification, permeability tests were conducted. e glass beads and quality-controlled standard sand and soils obtained from fields were used for the specimen. e experiment results showed that the hydraulic conductivity of the soil mixture strongly depends on the mixing ratio. As the amount of the coarse soil contained in the fine soil increased, the hydraulic conductivity of the mixture decreased from that for the fine soil until the critical mixing ratio. is ratio is defined as the fine soils perfectly fill the voids between the coarse soils without remains. When the ratio is greater than the critical mixing ratio, the hydraulic conductivity is drastically increased with the mixing ratio up to that of the coarse soil. e comparison between the computed values and the test results shows that the introduced model successfully describes the measurements.

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Hydraulic Conductivity of Soils from Grain-Size Distribution: New Models

Cited by 124 publications

References 17 publications

The use of statistical grain-size method in analysing borehole and evaluating aquifer parameters. A case study of Ajali Sandstone formation, southeastern Nigeria

The use of statistical grain-size method in analysing borehole and evaluating aquifer parameters. A case study of Ajali Sandstone formation, southeastern Nigeria

Defining Soil Materials for 3-D Models of the Near Surface: Preliminary Findings

Hydraulic Conductivity Variation of Coarse‐Fine Soil Mixture upon Mixing Ratio

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