Advanced mathematical models and their comparison to predict compaction properties of fine-grained soils from various physical properties

Omar, Maher; Shanableh, Abdallah; Mughieda, Omer; Arab, Mohamed G.; Zeiada, Waleed; Al‐Ruzouq, Rami

doi:10.1016/j.sandf.2018.08.004

Cited by 20 publications

(8 citation statements)

References 15 publications

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“…This combination might not always be the case as all factors are unlikely to affect the compaction characteristics and interact with one another. Consequently, frequent updates are required for the model to accurately estimate the OMC and MDD [26,13,14].…”

Section: Multiple Linear Regression Modelmentioning

confidence: 99%

Soft Computing Models to Predict the Compaction Characteristics from Physical Soil Properties

Ali

2023

ETJ

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Engineering and Technology Journal 41 (05) (2023) 2 of soil with such simple physical properties as Atterberg limits and particle-sizes. These are obtained through simple index tests [3].The determination of compaction characteristics indirectly considering the physical soil properties has been an ongoing topic of many studies. Several correlations have been developed to estimate the compaction characteristics through individual soil index properties [3 -12] or from considering soil fractions [13,14]. However, other studies indicated that considering one input parameter might not be sufficient to estimate the compaction characteristics. Therefore, multiple linear regression (MLR) models were utilized to predict the compaction characteristics considering different basic soil parameters [15 -17, 14, 2, 18, 19]. Further, machine learning techniques were used in some other studies to develop more accurate correlations [20 -23]. Sinha and Wang [24], as an early study employing artificial neural networks (ANN) for the prediction of soil compaction characteristics, indicated that the ANN technique outperforms the traditional statistical models, and a reliable prediction can be obtained. Sivrikaya [16] used a multilinear regression model (MLR) to predict the compaction characteristics of fine-grained soils from soil index properties and soil particle -sizes. The considered input parameters were the combination of gravel content (G), sand content (S), fine-grained content (F), plasticity index (PI), liquid limit (LL), and plastic limit (PL). The study concluded that the compaction characteristics can correlate with Plastic limit well compared to other index properties. Gunaydin [4] employed different techniques: simple-multiple analysis and artificial neural networks in order to predict the compaction characteristics based on the soil particle -sizes. The study highlights that, from both techniques, reliable correlations (R 2 = 0.70 -0.95) for preliminary design can be achieved.Furthermore, Mujtaba et al. [17] developed multiple regression analysis models for 110 sandy soils to predict the compaction characteristics according to the uniformity coefficient (Cu) and compaction energy (CE). Considering the liquid limit (LL), the plasticity index (PI), and compaction energy (CE), Tenpe and Kaur [25] investigated artificial neural network (ANN) modeling performance for predicting compaction characteristics with respect to the index properties of soil. Moreover, Omar et al. [26] utilized complicated mathematical models and novel approaches to anticipate the compaction characteristics of fine-grained soil from numerous physical properties. In addition, Farooq et al. [2] utilized a multiple regression model to predict OMC. Also, Saika et al. [8] developed a set of regression models for predicting compaction characteristics with respect to consistency limits.Using ANNs and MLR for 728 datasets, Karimpour et al. [27] employed some models to predict the compaction characteristics based on soil type, grain size distribution, l...

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Section: Multiple Linear Regression Modelmentioning

confidence: 99%

Soft Computing Models to Predict the Compaction Characteristics from Physical Soil Properties

Ali

2023

ETJ

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“…We describe below results obtained with the proposed EPR approach for two different case studies. Modeling of optimum moisture content (OMC) has important implications for the compaction characteristics of soils (Omar et al, 2018;Gomes et al, 2021b), while the modeling of creep index (C 𝛼 ) is fundamental for a variety of constitutive models used in engineering (Karim and Lo, 2020;Yin et al, 2011).…”

Section: Modeling Of Soil Propertiesmentioning

confidence: 99%

A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection

Gomes

Vrugt

2022

Engineering Applications of Artificial Intelligence

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“…Many researchers have utilized ANNs to predict liquefaction [ 27 ], compaction properties of fine-grained soils [ 28 ], mixing soil [ 29 , 30 ], displacement at selected points of the clayey cover of the landfill model [ 4 ], soil thermal conductivity [ 31 ], and soil compaction [ 32 ]. In addition to geotechnical engineering, artificial neural networks have been applied in many fields of engineering [ 33 ], and to improve the accuracy of ANN predictions, genetic algorithms combined with ANNs [ 34 ] and deep learning combined with ANNs [ 35 ] have been applied.…”

Section: Introductionmentioning

confidence: 99%

The use of polyurethane foam-sand mixtures in sandy embankment design- predicting seismic response using FEM, catastrophe theory, B-spline method, and artificial neural networks

Mughieda,

Namdar,

Nie

2024

Heliyon

Self Cite

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Advanced mathematical models and their comparison to predict compaction properties of fine-grained soils from various physical properties

Cited by 20 publications

References 15 publications

Soft Computing Models to Predict the Compaction Characteristics from Physical Soil Properties

Soft Computing Models to Predict the Compaction Characteristics from Physical Soil Properties

A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection

The use of polyurethane foam-sand mixtures in sandy embankment design- predicting seismic response using FEM, catastrophe theory, B-spline method, and artificial neural networks

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