Simplifying the Analysis of Soil Particle Sizes. I. Test of the Sur and Kukal's Modified Hydrometer Method

Adiku, S.G.K.; Osei, Gabriel; Adjadeh, T. A.; Dowuona, G. N.

doi:10.1081/lcss-200026828

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Based on the literature review, various methods had been proposed by researchers to determine the fine particle distributions. Grain-size analysis by laser diffractometry study [3], particle image processing by image processing technique [4], classification system for soil texture using laser diffraction method [5], conductivity of soil-water mixture [6] and automated system based on image [7] are some of latest study to develop better accuracy and fast method in fine particle size distribution analysis.…”

Section: Introductionmentioning

confidence: 99%

Determination of Silt and Clay soil particle distribution using new Silt-Clay Separation Quick method

Ibrahim,

Ismail,

Abdul Razak

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Hydrometer method is the standard method that normally used to determine fine particle size distributions for soil classification purposes. This study aims to develop new method that enable quick determination of silt-clay particle distribution compared to the hydrometer method that requires procedure of pretreatment and additional 24 hours required for the complete procedure of the test. The various soil samples were collected from seven different locations including Sunway Carnival (Marine Clay), Suling Hills (Residual Clay), Jawi Quarry (Residual Clay), Kelantan River (River Clay), Batu Kawan (Marine Clay), Kaolin Clay and Sungai Petani (Residual Clay). Three soil samples from each site were prepared for the hydrometer method and additional three samples for the new method silt-clay separation. Sample preparation and testing is repeated for all samples from seven site locations. The results of silt clay particle distribution obtained from both experimental works were compared to analyze the compatibility of data. The data analysis shows that there are no significant differences (P > 0.05) between the two methods in all examined soil samples. This indicates strong evidence to support this research that the silt-clay separation new method is comparable to the standard hydrometer method. Regression analysis between the two methods produced a coefficient of determination (R-square) values of 0.9912 respectively. The straight-line equation generated for all data produced the gradient equal to 0.8465, which is close to 1 indicates stronger evidence to supports the accuracy of the new method. In conclusion, the preliminary results of new method silt-clay separation confirmed its reliability to analyze the particle soil distribution in silt-clay fraction with 99.12% data accuracy compared to the standard hydrometer method.

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Section: Introductionmentioning

confidence: 99%

Determination of Silt and Clay soil particle distribution using new Silt-Clay Separation Quick method

Ibrahim,

Ismail,

Abdul Razak

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The use of artificial neural networks in the determination of soil grain composition

Sekuła

Karłowska-Pik

Kmiecik

2023

Stoch Environ Res Risk Assess

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The paper presents the possibility of using data mining tools — artificial neural networks — in prediction of hydrometer reading after 24 h in order to limit the duration of the test to 4 h. The authors analysed a database of 693 granulometric composition analysis results of genetically different soils with the use of radial basis function network (RBF) and multilayer perceptron (MLP). The calculations performed showed that it is possible to use MLP to shorten the test time without affecting the quality of the results. The presented accuracy of the model, in the range of 0.55–0.72, allows one to determine the content of silt and clay fractions with an accuracy of 0.49% for equivalent diameter (dT) and 1.50% for percentage of all particles with a diameter smaller than dT (ZT). The results were better than that achieved using linear re-gression models with all predictors (REG), stepwise regression models (SREG), and classification and regression trees (CRT). Taking into account the uncertainty of hydrometric determinations, the obtained forecast values is lower than this uncertainty, therefore neural networks can be used to predict the results of this type of research.

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Simplifying the Analysis of Soil Particle Sizes. I. Test of the Sur and Kukal's Modified Hydrometer Method

Cited by 2 publications

References 5 publications

Determination of Silt and Clay soil particle distribution using new Silt-Clay Separation Quick method

Determination of Silt and Clay soil particle distribution using new Silt-Clay Separation Quick method

The use of artificial neural networks in the determination of soil grain composition

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