Estimation of Compaction Parameters Based on Soil Classification

Lubis, Alida; Muis, Z A; Hastuty, Ika Puji; Siregar, Irdian

doi:10.1088/1757-899x/306/1/012005

Cited by 8 publications

(4 citation statements)

References 1 publication

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“…Based on the soil classification of USCS, the data obtained was the liquid limit value of 52.43%, and the percentage of soil passing the sieve no.200 was 51.38%; thus, the soil samples were included in the CL group that was inorganic clay with low to medium plasticity (Lubis et al, 2018).…”

Section: Physical Properties Of Soilmentioning

confidence: 99%

Comparison of the Use of Cement, Gypsum, and Limestone on the Improvement of Clay through Unconfined Compression Test

Hastuty

2019

Journal of the Civil Engineering Forum

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Soil stabilization is an effort to improve soil properties by adding additives in the soil to increase the soil strength and maintain the shear strength of the soil. There are many materials which can be used as stabilizers. The materials used in this study were cement, gypsum, and limestone, then the compressive strength values were compared by using the Unconfined Compression Test (UCT). The mixture combinations used in this study were 1% to 10% of cement, gypsum, and limestone on clay by curing for 14 days. The compressive strength value resulted from the unconfined compression test on the original soil sample was 1.4 kg/cm2. The original soil was classified as moderately sensitive soil because the sensitivity value of the original soil was 2. After being stabilized with various mixtures of cement, gypsum, and limestone, soil stabilization using cement obtained the maximum unconfined compressive strength value is 3.681 kg/cm2 in the mixture of 10%. Similarly, the soil stabilization using limestone and gypsum also obtained its maximum unconfined compressive strength value in the mixture of 10% is 3.307 kg/cm2 and 2.975 kg/cm2, respectively.

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Section: Physical Properties Of Soilmentioning

confidence: 99%

Comparison of the Use of Cement, Gypsum, and Limestone on the Improvement of Clay through Unconfined Compression Test

Hastuty

2019

Journal of the Civil Engineering Forum

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“…200 is 75.04 percent, and the liquid limit value is 48.65%, a plot on the graph is made to determine the soil classification. The soil obtained is included in the CL group, namely inorganic clay with low to moderate plasticity [25].…”

Section: Physical Properties Of the Soilmentioning

confidence: 99%

The Effect of Gypsum Treated Clay as a Road Subgrade Material

Halim¹

2022

GEOMATE

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Due to clay soil's low bearing capacity and shear strength, mechanical and chemical stability are required. The objective of this study was to increase the California bearing ratio (CBR) value of clay as a subgrade material by utilizing gypsum waste and determining the percentage of gypsum waste added to the soil to produce an optimal CBR value. According to the USCS classification, the soil sample used in this study was a CL soil sample (Clay-Low Plasticity). Gypsum waste is used as a stabilizer in varying proportions of 10%, 15%, 20%, and 25%, based on the weight of the soil sample. The untreated soil's maximum dry density (MDD) was 1.53%, and the maximum water content was 23.91%. The addition of 25% gypsum resulted in a maximum dry density of 1.571 g/cm3 and an optimum water content of 18.95%. Furthermore, 25% gypsum-treated soil increased the value of both soaked and unsoaked CBR. The addition of 17% gypsum resulted in the best CBR value. The Unconfined Compression Test results revealed that the highest compressive strength (qu) was obtained at 21 days cured and 17% gypsum, with an MDD value of 1.593 kg/cm 2 .

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“…However, this method can only make preliminary predictions of soil MDD and OMC and has a large error [16][17][18][19][20]. Multiple linear regression allows the creation of empirical equations to predict the OMC and MDD of soils [21,22]. Empirical equations may affect the reliability of prediction due to the presence of multiple factors, and empirical equations can only reflect linear soil behavior, not complex nonlinear soil behavior.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Compaction Parameters Using Machine Learning Models

Li,

You,

et al. 2024

Applied Sciences

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Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) are two important parameters of soil filling, which affect the soil stability and bearing capacity, and thus the reliability and durability of facilities such as highways and bridges. Therefore, it is important to make reasonable predictions of OMC and MDD. Four machine learning algorithms, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting Tree (XGBoost), are adopted in this paper to establish MDD and OMC prediction models. After training and testing, the best models of the four algorithms are compared. The results show that, as an ensemble learning algorithm, XGBoost is the best model for predicting MDD and OMC, with an R2 of 0.9234 for OMC, and an R2 of 0.9098 for MDD. Finally, the feature importance analysis concludes that the plastic limit (PL) and the liquid limit (LL) are the two features that affect OMC and MDD the most. The prediction of soil compaction parameters using machine learning models, especially ensemble learning, can significantly reduce the amount of laboratory work and improve the efficiency of optimizing design for soil resource utilization in engineering construction.

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Estimation of Compaction Parameters Based on Soil Classification

Cited by 8 publications

References 1 publication

Comparison of the Use of Cement, Gypsum, and Limestone on the Improvement of Clay through Unconfined Compression Test

Comparison of the Use of Cement, Gypsum, and Limestone on the Improvement of Clay through Unconfined Compression Test

The Effect of Gypsum Treated Clay as a Road Subgrade Material

Prediction of Soil Compaction Parameters Using Machine Learning Models

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