Effects of particle breakage on the mechanical characteristics of geogrid-reinforced granular soils under triaxial shear: A DEM investigation

Shi, Danda; Niu, Jinzhong; Zhang, Jiao; Chao, Zhiming; Fowmes, Gary John

doi:10.1016/j.gete.2023.100446

Cited by 20 publications

(5 citation statements)

References 85 publications

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“…This inadequacy is primarily due to the unique geo-structural characteristics of marine clay sediments. Moreover, a significant limitation of most existing approaches is their failure to account for the effects of cyclic loading and unloading of confinement pressure [32][33][34][35]. Field tests pose challenges for quantifying the permeability anisotropy of marine clay sediments containing cracks, let alone determining their stress-dependent permeability tensors.…”

Section: Engineering Applicationmentioning

confidence: 99%

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

Cui,

Zhou,

Gao

et al. 2024

Water

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In this study, a method was introduced to validate the presence of a Representative Elementary Volume (REV) within marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure. Physical testing provided the basis for this verification. Once the existence of the REV for such sediment was confirmed, we established a machine-learning predictive model. This model utilizes a hybrid algorithm combining Particle Swarm Optimization (PSO) with a Support Vector Machine (SVM). The model was trained using a database generated from the aforementioned physical tests. The machine-learning model demonstrates favorable predictive performance based on several statistical metrics, including the coefficient of determination (R2), mean residual error (MSE), mean relative residual error (MRSE), and the correlation coefficient R during the verification process. Utilizing the established machine-learning predictive model, one can effortlessly obtain the permeability tensor of marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure by inputting the relevant stress condition parameters. The original research cannot estimate the permeability tensor under similar loading and unloading conditions through REV. In this study, the physical model test was used to determine the REV of marine cohesive sediments with cracks by cyclic-constrained pressure loading and unloading. Referring to the results of physical tests, we developed a machine-learning prediction model that can easily estimate the permeability tensor of marine cohesive sediments with cracks under cyclic loading and constrained pressure unloading conditions. This method greatly saves time and computation and provides a direct method for engineering and technical personnel to predict the permeability tensor in this case.

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Section: Engineering Applicationmentioning

confidence: 99%

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

Cui,

Zhou,

Gao

et al. 2024

Water

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“…Among the many current soil improvement methods, geosynthetics are commonly used in sand reinforcement in engineering and are widely used on slopes [2][3][4][5], retaining walls [6][7][8][9], dams, highways, railroad beds, and other projects. However, traditional soil reinforcement methods such as the unidirectional laying of geogrids [10][11][12][13][14][15][16], geotextiles [17][18][19][20][21][22][23], and other reinforcement materials can easily form potentially weak surfaces in the soil and increase its anisotropy. With the development 1335 (2024) 012011 IOP Publishing doi:10.1088/1755-1315/1335/1/012011 2 of the fiber industry, cost-effective chemical fiber products have been increasingly used in the engineering field; thus, fiber reinforcement as a new type of geotechnical reinforcement technology has received wide attention.…”

Section: Introductionmentioning

confidence: 99%

Analysis of basalt fiber reinforcement on static shear properties of Beibu Gulf sea sand

Wang,

He,

Tang

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Basalt fibers are a reinforcing material with excellent mechanical properties and durability. In contrast, although Beibu Gulf sea sand is widely in engineering, it exhibits low strength and poor stability, which can be improved by adding basalt fibers. In this study, the effects of fiber content, fiber length, and effective confining pressure on the static shear strength of fiber-reinforced sea sand were investigated using a triaxial shear test. The maximum improvement on the static shear characteristics and deformation resistance of sea sand were achieved for a fiber content and length of 0.8% and 12 mm, respectively. The cohesion and internal friction angle of sea sand were improved and the secant modulus and strain before and after basalt fiber reinforcement showed a nonlinear attenuation tendency. The reinforcement effect coefficient R and the basalt fiber content under different dosages were in accordance with the law of the Gaussian function. The value of R conformed to a linear growth and exponential function law under different fiber lengths and effective confining pressures, respectively. This study provides a solid theoretical basis for the sustainable utilization of sea sand resources and fiber reinforcement for road and coastal protection engineering in the Beibu Gulf region.

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“…These changes can induce drying-wetting cycles in the marine soft clay sediment, causing frequent expansion and shrinkage, which can reduce the clay peak shear strength [18][19][20]. Furthermore, the thermal reactions resulting from the climatic changes can cause temperature variations, which can either strengthen or weaken the mechanical properties of the soil [21][22][23]. Additionally, inherent properties of the marine soft clay sediment, such as soil density, moisture content, and other factors, can also have an impact on its peak shear strength [10,[24][25][26].…”

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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Effects of particle breakage on the mechanical characteristics of geogrid-reinforced granular soils under triaxial shear: A DEM investigation

Cited by 20 publications

References 85 publications

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

Analysis of basalt fiber reinforcement on static shear properties of Beibu Gulf sea sand

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

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