A DEM-based Generic Modeling Framework for Hydrate-Bearing Sediments

Wang, Pei; Xu, Chengkai; Yin, Zhen-Yu; Song, Shun-xiang; Xu, Changjie; Dai, Sheng

doi:10.1016/j.compgeo.2024.106287

Cited by 11 publications

(1 citation statement)

References 52 publications

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“…Meanwhile, Chao et al [51] applied a hybrid Support Vector Machine (SVM) model to assess soil shear strength, and Xu et al [52] developed a Particle Swarm Optimization (PSO) optimized Support Vector Machine (SVM) model for the same purpose. Despite these advancements, existing research on machine learning models for soil peak shear strength exhibits certain deficiencies that necessitate further refinement [53,54]. Firstly, soil shear strength modeling often ignores the effects of environmental factors such as dry and wet cycles and temperature, thus limiting its ability to assess the peak shear strength of marine soft clay sediments under real-world conditions of use [55,56].…”

Section: Introductionmentioning

confidence: 99%

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

Hu,

Li,

Wang

et al. 2024

Water

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In the design of offshore engineering foundations, a critical consideration involves determining the peak shear strength of marine soft clay sediment. To enhance the accuracy of estimating this value, a database containing 729 direct shear tests on marine soft clay sediment was established. Employing a machine learning approach, the Particle Swarm Optimization algorithm (PSO) was integrated with the Adaptive Boosting Algorithm (ADA) and Back Propagation Artificial Neural Network (BPANN). This novel methodology represents the initial effort to employ such a model for predicting the peak shear strength of the soil. To validate the proposed approach, four conventional machine learning algorithms were also developed as references, including PSO-optimized BPANN, Support Vector Machine (SVM), BPANN, and ADA-BPANN. The study results show that the PSO-BPANN model, which has undergone optimization via Particle Swarm Optimization (PSO), has prediction accuracy and efficiency in determining the peak shear performance of marine soft clay sediments that surpass that offered by traditional machine learning models. Additionally, a sensitivity analysis conducted with this innovative model highlights the notable impact of factors such as normal stress, initial soil density, the number of drying–wetting cycles, and average soil particle size on the peak shear strength of this type of sediment, while the impact of initial soil moisture content and temperature is comparatively minor. Finally, an analytical formula derived from the novel algorithm allows for precise estimation of the peak shear strength of marine soft clay sediment, catering to individuals lacking a background in machine learning.

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Section: Introductionmentioning

confidence: 99%

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

Hu,

Li,

Wang

et al. 2024

Water

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Hydromechanical analysis of slurry infiltration with coupled CFD–DEM method

Cheng,

Wang,

Wang

et al. 2024

Num Anal Meth Geomechanics

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The tunnel face stability is closely related to the migration behavior and clogging mechanism of the slurry particles in granular soils, which is not yet fully understood. In this study, the coupled computational fluid dynamics–discrete element method (CFD–DEM) is adopted to investigate the infiltration behavior of slurry at the particle scale. The full Johnson–Kendall–Roberts (JKR) contact model describing interparticle behavior that accounts for cohesion present even when particles are microseparated after collisions is adopted for clay‐clay or clay‐sand particles and the Hertz–Mindlin model is used for sand‐sand particles. Through simulations, the mechanism of the slurry particle's blockage is identified as physical, frictional and cohesive modes, and slurry particle migration usually undergoes the process of aggregation‐destruction‐migration‐reassembly. Meanwhile, the influences of pressure, cohesive force, and particle shape on infiltration are also analyzed. The results indicate that high pressure lengthens the infiltration path and increases the infiltration rate. The increase in cohesion enhances the effective clogging of the sand column by the slurry, and particles with smaller sphericity are more likely to clog in the sand column. These findings may enhance our understanding of the slurry infiltration mechanism and its practical application in TBM tunneling.

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Investigation of Suffusion Under Torsional Shear Conditions With CFD‐DEM

Song,

Yin,

Liu

et al. 2024

Num Anal Meth Geomechanics

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This study investigates, for the first time ever, the suffusion on gap‐graded granular soils under torsional shear conditions from a microscopic perspective. A numerical model of the hollow cylinder torsional shear test (HCTST) using the discrete element method (DEM) is first developed, where an algorithm for simulating the real inner and outer rubber membranes of the hollow cylinder apparatus (HCA) is introduced. After the validation, the computational fluid dynamics (CFD) approach is introduced for the coupling between the particle and fluid phases. Then, a series of the coupled CFD‐DEM suffusion simulations considering the rotation of the major principal stress axis (α) and intermediate principal stress ratio (b) are conducted. It is found that more fine particles are eroded in cases having smaller α and b, and the clogging phenomenon in the middle zones becomes more significant as both α and b increase. From the microscopic perspective, the specimens whose contact anisotropy principal direction is close to the fluid direction will lose more fines, and the anisotropy magnitude also plays an important role. In addition, the differences in structure and vertical connectivity of the pores in HCTST samples under various complex loading conditions cause fine particles to have different migration paths, further resulting in different fines mass loss.

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A DEM-based Generic Modeling Framework for Hydrate-Bearing Sediments

Cited by 11 publications

References 52 publications

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

Hydromechanical analysis of slurry infiltration with coupled CFD–DEM method

Investigation of Suffusion Under Torsional Shear Conditions With CFD‐DEM

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