A new procedure for determining dry density of mixed soil containing oversize gravel

Aghajani, Hamed Farshbaf; Yengejeh, Masoud Ghodrati; Karimzadeh, Amirmohammad; Soltani-Jigheh, Hossein

doi:10.1007/s11771-018-3957-7

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…This indicated a significant effect of specific gravity on the predicted maximum dry density [3][4][5][6]. It can be said that an oversized specific gravity has the major effect of underestimating the predicted MDD compared with a minor impact on the fine content [17]. Meanwhile, applying Eq.…”

Section: Predict Maximum Dry Density Using Fine Content and Specific Gravity Correlationsmentioning

confidence: 94%

Maximum dry density of sand–kaolin mixtures predicted by using fine content and specific gravity

Alshameri

2020

SN Appl. Sci.

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Numerous studies have covered the comparison of maximum dry density (MDD) toward the combination of soil properties and compaction parameters. These combinations are dominated by Atterberg limits, moisture content, and compaction effort. Nevertheless, minimal efforts have been expended in predicting the MDD using two simple soil properties, i.e., fine content (FC) and specific gravity (G s ). Sand-kaolin mixtures were compacted and then used to examine the correlation between MDD, FC, and G s through different moisture content, and FC ranged from 20 to 70%. The results showed the importance of considering both G s and FC in the correlation estimation in order to reduce the uncertainty of using only FC as a single parameter. A linear relationship was obtained with an excellent correlation coefficient (R 2 = 0.99) between MDD and the ratio of FC/G s 2 . The empirical correlation equation was justified using data from 163 measured MDD obtained from previous studies. All predicted MDD were within the confidence interval of ± 10, which confirmed a low error range of the predicted MDD. The application of the equation was limited to FC and FC/G s less than 77% and 0.302, respectively. It should, however, be noted that the presence of a high amount of gravel in the soil mixture will not be applicable for this correlation.

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Section: Predict Maximum Dry Density Using Fine Content and Specific Gravity Correlationsmentioning

confidence: 94%

Maximum dry density of sand–kaolin mixtures predicted by using fine content and specific gravity

Alshameri

2020

SN Appl. Sci.

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Prediction of resilient modulus for subgrade soils based on ANN approach

Zhang

Peng

et al. 2021

J. Cent. South Univ.

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：The resilient modulus (MR) of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design. In order to determine the resilient modulus of compacted subgrade soils quickly and accurately, an optimized artificial neural network (ANN) approach based on the multi-population genetic algorithm (MPGA) was proposed in this study. The MPGA overcomes the problems of the traditional ANN such as low efficiency, local optimum and over-fitting. The developed optimized ANN method consists of ten input variables, twenty-one hidden neurons, and one output variable. The physical properties (liquid limit, plastic limit, plasticity index, 0.075mm passing percentage, maximum dry density, optimum moisture content), state variables (degree of compaction, moisture content) and stress variables (confining pressure, deviatoric stress) of subgrade soils were selected as input variables. The MR was directly used as the output variable. Then, adopting a large amount of experimental data from existing literature, the developed optimized ANN method was compared with the existing representative estimation methods. The results show that the developed optimized ANN method has the advantages of fast speed, strong generalization ability and good accuracy in MR estimation.

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An Experimental and Machine-Learning Investigation into Compaction of the Cemented Sand-Gravel Mixtures and Influencing Factors

Aghajani

Karimi

Diznab

2022

Transp. Infrastruct. Geotech.

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A new procedure for determining dry density of mixed soil containing oversize gravel

Cited by 5 publications

References 24 publications

Maximum dry density of sand–kaolin mixtures predicted by using fine content and specific gravity

Maximum dry density of sand–kaolin mixtures predicted by using fine content and specific gravity

Prediction of resilient modulus for subgrade soils based on ANN approach

An Experimental and Machine-Learning Investigation into Compaction of the Cemented Sand-Gravel Mixtures and Influencing Factors

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