Evaluation of dependency of compression index on toughness limit for fine-grained soils

Shimobe, Satoru; Karakan, Eyyüb; Sezer, Alper

doi:10.1007/s00521-023-08292-8

Cited by 2 publications

(1 citation statement)

References 86 publications

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“…At all energy levels studied, they found that the maximum dry unit weight displayed a good correlation with the dry unit weight at the w T water content. Additionally, Shimobe et al [54] investigated w T and a soil state index parameter defined as SSI = w T × e 0 , where e 0 is the initial void ratio, as predictors of the compression index (C c ) parameter. The inclusion of e 0 means that the SSI takes into account the effects of several index properties, including the initial density and consistency of soils.…”

Section: Soil Remolding Toughness Measurement 21 Barnes' Thread Rolli...mentioning

confidence: 99%

A Comprehensive Review of Soil Remolding Toughness Determination and Its Use in the Classification of Fine-Grained Soils

2023

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The remolding toughness property of fine-grained soil has not been investigated that much, mainly because it has not lent easily to direct measurement, with soil toughness usually qualitatively described. In practical terms, as the plastic limit wP is approached, tougher soils require greater rolling effort during the wP test, such that plasticity and toughness properties can be used to distinguish those plastic soils having greater deformation resistance for various field applications. This state-of-the-art review paper presents a critical appraisal of soil remolding toughness determination and its limited use, to date, in the classification of fine-grained soils. The recent developments reviewed and critically assessed include mechanical thread rolling for nominal toughness measurement during the wP rolling-out procedure, various extrusion approaches, and proposed correlations between toughness and the plasticity index to liquid limit ratio. From statistical analysis of previously reported toughness–consistency limits data, some new correlations are introduced in the present paper. Soil classification using the traditional Casagrande plasticity chart is not entirely accurate for certain soil types in that one can observe soils that present high toughness (something typical of clay) being incorrectly classified as silt soil. From this perspective, a new toughness chart is introduced to augment (or for use instead of) the Casagrande plasticity chart in obtaining more reliable soil classification. This paper concludes with recommendations on future research efforts for routinely obtaining soil toughness measurements.

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Section: Soil Remolding Toughness Measurement 21 Barnes' Thread Rolli...mentioning

confidence: 99%

A Comprehensive Review of Soil Remolding Toughness Determination and Its Use in the Classification of Fine-Grained Soils

2023

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Predictive models for treated clayey soils using waste powdered glass and expanded polystyrene beads using regression analysis and artificial neural network

Akis,

Cigdem

2024

Neural Comput & Applic

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Waste materials contribute to a wide range of environmental and economic problems. To minimize their effects, a safe strategy for reducing such negative impact is required. Recycling and reusing waste materials have proved to be effective measures in this respect. In this study, an eco-friendly treatment is investigated based on using waste powdered glass (WGP) and EPS beads (EPSb) as mechanical and chemical admixers in soils. For this purpose, Atterberg limit, standard proctor, free swell, and unconfined compression tests are performed on soil samples with different ratios of waste materials at their optimum moisture contents. The obtained test results indicate that adding WGP to cohesive soils increases the unconfined compressive strength (UCS) and reduces free swell (FS). In contrast, using EPSb reduces both FS and UCS of the treated soil samples. An optimum combination of both waste materials is determined for the improvement of the properties of high plasticity clay used in this study. Furthermore, multiple linear regression (MLR) and artificial neural network (ANN) methods are used to predict the FS and UCS of the clayey soils based on the data obtained here and the experimental test results reported in the literature. Once the FS and UCS values of untreated soil and additive percentages are defined as independent variables, both methods are shown to predict the FS and UCS values of the treated soil samples on a satisfactory level with the coefficient of correlation ($${R}^{2}$$ R 2 ) values greater than 0.926. Additionally, when only the index properties (liquid limit, plastic limit, and plasticity index) of the soil samples with waste materials are used as dependent variables, the $${R}^{2}$$ R 2 values obtained by the ANN method are 0.968 and 0.974 for FS and UCS, respectively. The results of the untreated soil samples' FS and UCS tests are known, and the linear regression and ANN techniques yield similar results. Lastly, the ANN method is used to predict the FS and UCS of the treated samples in accordance to the limited predictors (e.g., only the Atterberg limits of the soil sample).

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Evaluation of dependency of compression index on toughness limit for fine-grained soils

Cited by 2 publications

References 86 publications

A Comprehensive Review of Soil Remolding Toughness Determination and Its Use in the Classification of Fine-Grained Soils

A Comprehensive Review of Soil Remolding Toughness Determination and Its Use in the Classification of Fine-Grained Soils

Predictive models for treated clayey soils using waste powdered glass and expanded polystyrene beads using regression analysis and artificial neural network

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