Modified Duncan-Chang Constitutive Model for Modeling Supported Excavations in Granular Soils

Jia, Pengjiao; Khoshghalb, Arman; Chen, Chen; Zhao, Wen; Dong, Manman; Esgandani, Golnaz Alipour

doi:10.1061/(asce)gm.1943-5622.0001848

Cited by 16 publications

(9 citation statements)

References 38 publications

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“…Besides, the consolidation rate of this CCF with the gravel cushion is higher than that of the CCF without the gravel cushion. The consolidation rate of the CCF reinforced with penetrated PCCSs and DM columns increases obviously compared with the traditional CCF improved by PC piles united with DM columns (5) The additional stress of subsoils in the analytical method was simplified as the linear variation along with the depth, which may be different from that in situ. The further research will be conducted to improve the current method…”

Section: Conclusion and Limitationmentioning

confidence: 99%

“…The composite ground has been widely used in ground improvement due to its convenience and efficiency [1][2][3][4][5][6][7]. A variety of pile techniques have been developed to meet various engineering needs, such as stone columns, deep mixing (DM) columns, T-shaped DM columns, jet grouted columns, and precast cement (PC) piles [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Analytical Solution for Consolidation Behaviors of Combined Composite Foundation Reinforced with Penetrated PCCSs and Floating DM Columns

Zhang

Liu

et al. 2023

Geofluids

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As the composite pile, the precast concrete piles reinforced with cement-treated soil (PCCS) is formed by driving the precast cement (PC) pile into the deep mixing (DM) column, which has been successfully and widely utilized to support buildings and embankments over soft soil. To increase the pile spacing and give full play to the economic merits of the PCCS, a reinforcement scheme, which involves the combined use of rigid piles and flexible columns, employing penetrated PCCSs and floating DM columns is proposed and utilized for soft soil ground treatment. However, there is a lack of feasible method for consolidation behaviors of this combined composite foundation (CCF) reinforced with penetrated PCCSs and floating DM columns under flexible loads. This paper developed an analytical solution to predict the average consolidation degree of this CCF based on a cylinder consolidation model and double-layer ground consolidation theory. The excess pore pressure and average consolidation degree were calculated by considering the composite pile penetration into the cushion. The analytical method agrees well with results obtained by numerical analysis. Additionally, a parametric study was conducted systematically to analyze the effect of key influence factors on the average consolidation degree of this CCF. The results indicate that the consolidation rate of this CCF can be much faster than that of the natural ground. The consolidation rate strongly depends on the compressive modulus and area replacement ratio of PCCSs. The increasing inner core-outer core modulus ratio and the inner core-subsoil modulus ratio increase the consolidation rate of this CCF. In addition, the consolidation rate increases with the gravel cushion-subsoil modulus ratio, while it decreases with the loading period.

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Section: Conclusion and Limitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analytical Solution for Consolidation Behaviors of Combined Composite Foundation Reinforced with Penetrated PCCSs and Floating DM Columns

Zhang

Liu

et al. 2023

Geofluids

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“…In geotechnical engineering, there are numerous sophisticated soil models that are formed based on elastoplastic theory [ 14 ]. These models are theoretically rigorous, but they frequently ask for a number of expensive and challenging-to-quantify parameters [ 15 ]. Therefore, simpler models, such as the Duncan-Chang model proposed by Duncan and Chang, are preferable in some situations, especially when the stress path is not unduly convoluted or when there is a lack of sufficient data on the soil properties to conduct an exploratory investigation [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Influence of sand content on mechanical properties of sand-silt mixtures from check dam deposits in the Loess Hilly of Ningxia, China

Chang,

Wang,

Wang

2023

PLoS ONE

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An accurate description of the stress-strain relationships of sand-fine mixtures is very important to analyze the soil’s mechanical properties. Hence, a series of consolidated drained (CD) triaxial tests were performed on reconstructed sand-silt mixtures with sand contents of 0%, 16.67%, 28.57%, 50%, and 60% in the paper to examine the effect of the sand content on the stress-strain curves of the soil. Results show that for sand-fine mixtures with different sand contents, the stress-strain curves are also mainly strain softening though there exist different degrees of softening. In order to quantitatively describe the strain-softening characteristics of sand-fine mixtures, a modified Duncan-Chang model was developed. To verify the applicability of the modified mode, examples such as coral clay and undisturbed loess are described and predicted. There is a high consistency between theoretical and experimental values. Finally, a sand-content-dependent constitutive model that considered the effects of sand content and confining pressure was proposed based on the modified Duncan-Chang model by constructing the relationship between model parameters and confining pressure and sand content. The constitutive model was implemented in ABAQUS software and verified by comparing the calculated results with the triaxial test data of sand-fine mixtures under the confining pressure of 500 kPa. The comparison results indicate that the constitutive model can reflect the real characteristics of sand-fine mixtures.

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“…The data were fitted to the celebrated Duncan-Chang hyperbolic model [12], which has a successful history of application in soil mechanics despite its simplicity and is still being used (e.g., [25]). This model is defined by two model parameters, namely, the tangent elastic modulus E 0 and the ultimate deviatoric stress r ult .…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning for unraveling hidden correlations between particle size distributions and the mechanical behavior of granular materials

Tejada

Antolín

2021

Acta Geotech.

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A data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The discrete element method, DEM, was used to generate 92,378 sphere packings that covered many different kinds of particle size distributions, PSD, lying within 2 particle sizes. These packings were subjected to triaxial compression and the corresponding stress–strain curves were fitted to Duncan–Chang hyperbolic models. An artificial neural network (NN) scheme was able to anticipate the value of the model parameters for all these PSDs, with an accuracy similar to the precision of the experiment and even when the NN was trained with a few hundred DEM simulations. The estimations were indeed more accurate than those given by multiple linear regressions (MLR) between the model parameters and common geotechnical and statistical descriptors derived from the PSD. This was achieved in spite of the presence of noise in the training data. Although the results of this massive simulation are limited to specific systems, ways of packing and testing conditions, the NN revealed the existence of hidden correlations between PSD of the macroscopic mechanical behavior.

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Modified Duncan-Chang Constitutive Model for Modeling Supported Excavations in Granular Soils

Cited by 16 publications

References 38 publications

Analytical Solution for Consolidation Behaviors of Combined Composite Foundation Reinforced with Penetrated PCCSs and Floating DM Columns

Analytical Solution for Consolidation Behaviors of Combined Composite Foundation Reinforced with Penetrated PCCSs and Floating DM Columns

Influence of sand content on mechanical properties of sand-silt mixtures from check dam deposits in the Loess Hilly of Ningxia, China

Use of machine learning for unraveling hidden correlations between particle size distributions and the mechanical behavior of granular materials

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