Improving the prediction accuracy of small-strain shear modulus of granular soils through PSD: An investigation enabled by DEM and machine learning technique

Liu, Xingyang; Li, Zhanchao; Zou, Degao; Sun, Linsong; Ebrahim, Khailah Yahya; Liang, Jiaming

doi:10.1016/j.compgeo.2023.105355

Computers and Geotechnics

2023

DOI: 10.1016/j.compgeo.2023.105355

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Improving the prediction accuracy of small-strain shear modulus of granular soils through PSD: An investigation enabled by DEM and machine learning technique

Xingyang Liu

Zhanchao Li

Degao Zou

et al.

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2024

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Cited by 12 publications

References 70 publications

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Multi‐scale mechanics of polydisperse granular materials: From micro‐scale and wave propagation experiments to DEM analysis

Reddy,

He,

Senetakis

2024

Num Anal Meth Geomechanics

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This paper presents a multiscale experimental study integrated with numerical simulations examining the mechanics of polydisperse granular mixtures composed of coarse‐grained particles mixed with varying percentages of fines. The study includes macroscale wave propagation tests using bender elements on isotopically compressed granular samples to investigate the stiffness variation with changes in size ratio (SR) and fines content (FC). Empirical equations were developed to predict stiffness based on the experimental data, using the concept of equivalent void ratio to represent the influence of void ratio on stiffness. A newly introduced parameter called size disparity indicator was used to consider the coupled effects of size disparity and fines content on stiffness. Microscale assessment of the individual contacting grains revealed that stiffness (at grain scale) is affected by changes in grain size, even when the SR is less than 6, however, the experimental observations of the shear modulus behavior from the macroscale test results reveal minimal effect of FC. Furthermore, numerical simulations using the discrete element method were conducted on the polydisperse granular mixtures to demonstrate the coupled effect of SR and FC on the structural matrix formation, thereby influencing the force transfer among contacts resulting in varying stiffness behavior. Our findings provide valuable insights into the behavior of polydisperse granular mixtures in engineering applications.

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Multi‐scale mechanics of polydisperse granular materials: From micro‐scale and wave propagation experiments to DEM analysis

Reddy,

He,

Senetakis

2024

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

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Numerical Modeling on Small‐Strain Stiffness and Viscoelastic‐Viscoplastic Characteristic of Soft Soils

Ai,

Gu,

Yuan

2024

Num Anal Meth Geomechanics

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The behavior of soft soils distributed in coastal areas usually exhibits obvious time‐dependent behavior after loading. To reasonably describe the stress‐strain relationship of soft soils, this paper establishes a viscoelastic‐viscoplastic small‐strain constitutive model based on the component model and the hardening soil model with small‐strain stiffness (HSS model). First, the Perzyna's viscoplastic flow rule and the modified Hardin–Drnevich model are introduced to derive a one‐dimensional incremental Nishihara constitutive equation. Next, the flexibility coefficient matrix is utilized to extend the one‐dimensional model to three‐dimensional conditions. Then, by combining the HSS elastoplastic theory with the component model, the viscoelastic‐viscoplastic small‐strain constitutive model is subsequently established. To implement the proposed model for numerical analysis, the corresponding UMAT subroutine is developed using Fortran. After comparing the results of numerical simulations with those of existing literature, the reliability of the constitutive model and the program written in this paper is verified. Finally, numerical examples are designed to further analyze the effects of small‐strain parameters and viscoelastic‐viscoplastic parameters on the time‐dependent behavior of soft soils.

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State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

Liu,

Su,

Sun

et al. 2024

Artif Intell Rev

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Significant uncertainties can be found in the modelling of geotechnical materials. This can be attributed to the complex behaviour of soils and rocks amidst construction processes. Over the past decades, the field has increasingly embraced the application of artificial intelligence methodologies, thus recognising their suitability in forecasting non-linear relationships intrinsic to materials. This review offers a critical evaluation AI methodologies incorporated in computational mechanics for geotechnical engineering. The analysis categorises four pivotal areas: physical properties, mechanical properties, constitutive models, and other characteristics relevant to geotechnical materials. Among the various methodologies analysed, ANNs stand out as the most commonly used strategy, while other methods such as SVMs, LSTMs, and CNNs also see a significant level of application. The most widely used AI algorithms are Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), representing 35%, 19%, and 17% respectively. The most extensive AI application is in the domain of mechanical properties, accounting for 59%, followed by other applications at 16%. The efficacy of AI applications is intrinsically linked to the type of datasets employed, the selected model input. This study also outlines future research directions emphasising the need to integrate physically guided and adaptive learning mechanisms to enhance the reliability and adaptability in addressing multi-scale and multi-physics coupled mechanics problems in geotechnics.

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Improving the prediction accuracy of small-strain shear modulus of granular soils through PSD: An investigation enabled by DEM and machine learning technique

Cited by 12 publications

References 70 publications

Multi‐scale mechanics of polydisperse granular materials: From micro‐scale and wave propagation experiments to DEM analysis

Multi‐scale mechanics of polydisperse granular materials: From micro‐scale and wave propagation experiments to DEM analysis

Numerical Modeling on Small‐Strain Stiffness and Viscoelastic‐Viscoplastic Characteristic of Soft Soils

State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

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