A Review on the Resilient Response of Unsaturated Subgrade Soils

Chu, Xuanxuan

doi:10.1155/2020/7367484

Cited by 11 publications

(7 citation statements)

References 54 publications

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“…2 was developed, which successfully estimated the E of the coals. For the sake of clarity, the sub equation systems behind equation (2) were also presented in this paper (equations (3) to (10)), to let users implement the proposed model efficiently. According to the performance of the proposed ANN model, it was determined that the predicted and measured E values are in good agreement (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…As a result of the ANN analyses, the E for different coal types can be estimated using equation (2). The subfunctions of equation ( 2) were determined based on the deterministic approach previously described by Das [31] and they are listed in equations ( 3) to (10), where equations ( 9) to (10) are the normalization functions.…”

Section: Artificial Neural Network (Ann) Analysesmentioning

confidence: 99%

“…The stability of coal measures strata in underground mines is of prime importance in sustainable and safe coal mining operations. The stability of coal measures strata has been investigated mainly using numerical modeling techniques [1][2][3]. In numerical modeling of rock masses, several rock strength properties such as uniaxial compressive strength (UCS), Young modulus (E), and Poisson's ratio (v) are required as input parameters.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of deformation modulus of coals using artificial neural networks (ANN)

Köken

2022

Acta Tech. Jaurinensis

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In this study, the Young modulus (E) of different coals was investigated using artificial neural networks (ANN). For this purpose, a comprehensive literature survey was carried out to compile such datasets available for the ANN analyses. As a result of the literature survey, a database composed of 81 datasets was formed. In the ANN analyses, uniaxial compressive strength (UCS) and dry density (ρd) of coals were adopted as input parameters. The ANN analysis results demonstrated that the predictive model established in this study could be reliably used to estimate the E for different coals. The correlation of determination value (R2) for the developed model is 0.85, which shows its relative success. In this context, this study can be declared a case study showing the applicability of ANN for the evaluation of E for a wide range of coal types. However, the number of samples and independent variables should be increased to obtain more comprehensive models in future studies.

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Section: Resultsmentioning

confidence: 99%

Section: Artificial Neural Network (Ann) Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of deformation modulus of coals using artificial neural networks (ANN)

Köken

2022

Acta Tech. Jaurinensis

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“…Cyclic dry-wet conditions will result in the breakdown and buildup of soil particles [37,38]. This is because of the irreversible swelling and the increase in saturation degree caused by the dry-wet cycle that lead to instability of interlocking structure and a softening behavior of the soil [39]. After that, the soil is more susceptible to yield.…”

Section: Effect Of Dry-wet Cycle Numbermentioning

confidence: 99%

Research on Resilient Modulus Prediction Model and Equivalence Analysis for Polymer Reinforced Subgrade Soil under Dry–Wet Cycle

Luan,

Lu,

2023

Polymers

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The subgrade soil of asphalt pavement is significantly susceptible to changes in moisture content, and therefore many projects introduce polymer-based reinforcement to ensure soil performance. This paper aims to incorporate a variable representing the dry–wet cycle into the prediction model of resilient modulus of polymer reinforced soil. The polymer adopted is a self-developed subgrade soil solidification material consisting of sodium dodecyl sulfate and polyvinyl oxide. The current resilient modulus prediction model is improved, notably involving the effects of the dry–wet cycle. Combined with finite element method (FEM) analysis, the actual stress state of pavement and the coupling effect of dry–wet cycle and vehicle load on the resilient modulus are studied. The deterioration in resilient modulus with the variation in seasonal climate and load response is also investigated. Results show that the deviator stress is negatively correlated with the resilient modulus while the bulk stress has a linearly positive relation. The decreasing rate at low deviator stress is larger than that at the high level. Moreover, the dry–wet cycle can reduce the resilient modulus and the reducing amplitude is the largest at the first dry–wet cycle. FEM analysis shows that the middle position of the subgrade slope has the largest initial resilient modulus with decreasing amplitude in the first year of dry–wet cycles, while the upper position shows a smaller change. The variation in resilient modulus is closely related to the changes in cumulative volumetric water content. Considering that different positions of subgrade bear the external vehicle load, the equivalent resilient modulus is more realistic for guiding the subgrade design.

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“…Variations in moisture greatly affect the subgrade's resilient modulus, which, in turn, reduces soil stiffness and increases structural reactivity under pavement loads [1][2][3]. A large number of studies have been carried out to discuss the effects of moisture content [4,5], matrix suction [6,7], loading frequency [8,9], confining stress [10], and soil type [11][12][13] on the resilient modulus. In practice, it is difficult to take into account the influence of so many factors at the same time.…”

Section: Introductionmentioning

confidence: 99%

Calculation of and Key Influencing Factors Analysis on Equivalent Resilient Modulus of a Submerged Subgrade

Tang,

Chen,

et al. 2024

Materials

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To calculate and analyze the equivalent resilient modulus of a submerged subgrade, a constitutive model considering the effect of saturation and matrix suction was introduced using ABAQUS’s user-defined material (UMAT)subroutine. The pavement response under falling weight deflectometer (FWD) load was simulated at various water levels based on the derived distribution of the resilient modulus within the subgrade. The equivalent resilient modulus of the subgrade was then calculated using the equivalent iteration and weighted average methods. Based on this, the influence of the material and structural parameters of the subgrade was analyzed. The results indicate that the effect of water level rise on the tensile strain at the bottom of the asphalt layer and the compressive strain at the top of the subgrade is obvious, and its trend is similar to an exponential change. The equivalent resilient modulus of the subgrade basically decreases linearly with the rise in the water level, and there is high consistency between the equivalent iteration and weighted average methods. The saturated permeability coefficient and subgrade height have the most significant effect on the resilient modulus of the subgrade, which should be emphasized in the design of submerged subgrades, and the suggested values of the resilient modulus of the subgrade should be proposed according to the relevant construction conditions.

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A Review on the Resilient Response of Unsaturated Subgrade Soils

Cited by 11 publications

References 54 publications

Estimation of deformation modulus of coals using artificial neural networks (ANN)

Estimation of deformation modulus of coals using artificial neural networks (ANN)

Research on Resilient Modulus Prediction Model and Equivalence Analysis for Polymer Reinforced Subgrade Soil under Dry–Wet Cycle

Calculation of and Key Influencing Factors Analysis on Equivalent Resilient Modulus of a Submerged Subgrade

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