Discrete Element Modelling of Undrained Consolidated Triaxial Test on Cohesive Soils

Adajar, Joash Bryan; Ubay, Irene Olivia; Alfaro, Marolo C.; Chen, Ying

doi:10.1061/9780784482803.019

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…During the calibration process in numerical simulations, not only do parameters affect the outcomes, but there is also mutual influence among them [45]. A method to The boundary of the numerical specimen model consists of a cylindrical wall and upper and lower loading plates to prevent the particles from exceeding the boundary during the loading process by enlarging the cylindrical height by 1.2 times.…”

Section: Fine View Parameter Calibrationmentioning

confidence: 99%

“…During the calibration process in numerical simulations, not only do parameters affect the outcomes, but there is also mutual influence among them [45]. A method to appropriately reduce and control the number of parameters involves setting the stiffness ratio of soil particles and the parallel bond stiffness ratio to 1.5, and the parallel bond radius multiplier to 1.0.…”

Section: Fine View Parameter Calibrationmentioning

confidence: 99%

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Triaxial Test and Discrete Element Numerical Simulation of Geogrid-Reinforced Clay Soil

Wang,

Hu,

Liu

et al. 2024

Buildings

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Indoor triaxial tests on geogrid-reinforced clay elucidate the macroscopic changes in soil strength indices post-reinforcement, yet the underlying mechanisms of strength enhancement require further investigation. By conducting indoor triaxial tests and establishing a corresponding discrete element numerical model, we can delve into the fine-scale mechanisms of geogrid-reinforced soil. This includes analyzing changes in fine-scale parameters such as porosity, the coordination number, and contact stress between soil particles. The findings suggest that an increase in the number of geogrid reinforcement layers leads to a more pronounced improvement in peak strength and cohesion, albeit with minimal impact on the internal friction angle of the specimens. Furthermore, analysis of the triaxial test curves of reinforced soils indicates that the stress–strain relationship adheres to the Duncan–Chang model. Parameters derived from this model have been validated against experimental data, confirming their accuracy. The discrete element model was used to analyze the variations in fine-scale parameters such as porosity and coordination number. It revealed that reinforcement reduces the fluctuation amplitude of porosity and significantly increases the number of particle contacts, resulting in a denser soil structure. Further analysis of the change in contact stress between particles in the discrete element model revealed that the contact force between particles increased significantly after reinforcement and that the reinforcement played a role in restraining the soil particles and dispersing the reinforcement stress, which explains the increase in the strength of the mesh-reinforced clays from another perspective. This further elucidates the strength enhancement mechanism in geogrid-reinforced clay, offering a new perspective on the mechanical behavior and strength development of such materials.

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Section: Fine View Parameter Calibrationmentioning

confidence: 99%

Section: Fine View Parameter Calibrationmentioning

confidence: 99%

Triaxial Test and Discrete Element Numerical Simulation of Geogrid-Reinforced Clay Soil

Wang,

Hu,

Liu

et al. 2024

Buildings

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“…The goal of this study is to apply Machine Learning Modeling Competition to generate the soil profile, in the case of this study, Makati City, Philippines. Traditional regression technique is still practiced locally [9][10][11][12][13][14][15][16][17][18][19][20][21][22], however, there are Machine Learning models that have been successful [23][24][25] in estimating geotechnical parameters.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models to Generate a Subsurface Soil Profile: A Case of Makati City, Philippines

Galupino¹

2022

GEOMATE

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Soils and rocks are natural geomaterials, and a variety of spatially varying factors influence their properties during their complex geological formation phase. As a result, geomaterials can have different properties at different points on a given site. It is advantageous to be aware of the soil profile of a target location in advance to avoid duplication of tests, determining the borehole depth, and sampling methodology. This can result in more economical borehole testing. The goal of this study is to apply Machine Learning Modeling Competition to generate the soil profile, in the case of this study, Makati City, Philippines. The models competing include Tree Model, Discriminant Model, Naïve Bayes Model, k-Nearest neighbor, and Artificial Neural Network. Among the models, k-Nearest Neighbor Model resulted in the highest accuracy rate, for validation. As an additional output, the generated data was transformed into a soil profile delineation that was represented by soils that are grouped into various classes.

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“…Numerous efforts are being undertaken to close this gap by merging the quantification of soil parameters, the mapping of soil properties, and the mapping of hazard assessment in the Philippines [3][4][5][6][7][8]. Moreover, many empirical models were developed using traditional numerical and statistical techniques [9][10][11][12][13][14][15][16][17][18][19][20][21][22], however, there are still limitations, there are still areas of uncertainty or unreliability in the data, and the majority of them are not publicly available. By constructing models that create soil IOP Publishing doi:10.1088/1755-1315/1091/1/012021 2 parameters for unknown sites, geotechnical engineers may reliably anticipate the soil parameters they will encounter at their target locations.…”

Section: Introductionmentioning

confidence: 99%

Development of a k-Nearest Neighbor (kNN) Machine Learning Model to Estimate the SPT N-Values of Valenzuela City, Philippines

Galupino

Dungca

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Due to the intricate geological formation of geomaterials, they often exhibit a range of attributes at different sites on a given site. This has posed a problem for geotechnical engineers, as they require correct soil and rock information to plan and design geotechnical construction projects. Numerous efforts have been made at the local level to bridge this disparity by standardizing the quantification of soil parameters; nevertheless, these studies have limitations, and there are still areas with ambiguous or incorrect information. To help close this gap, this article will demonstrate an innovative technique for predicting representative Soil Penetration Test (SPT) N-values for each Valenzuela City Barangay/Zone using a k-Nearest Neighbor (k-NN) Machine Learning Model. Borehole data from the city of Valenzuela were collected and analyzed for this study. The k-NN Model’s input parameters are the borehole’s latitude, longitude, and depth, while the response parameters are the SPT N-Values. The k-NN technique was used to train the input data, identify patterns, and make classification decisions for the SPT N-Values per depth per location in Valenzuela City. The centroids of each Barangay/Zone in Valenzuela City were also extracted in Latitude and Longitude format and utilized to deploy the k-NN Model. To illustrate the findings, we presented subsurface information from Valenzuela City with SPT N-Values. For validation purposes, the accuracy rate of the machine learning model was obtained. The model’s hyperparameter in terms of k was tuned to determine if the accuracy rate could be improved.

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Discrete Element Modelling of Undrained Consolidated Triaxial Test on Cohesive Soils

Cited by 4 publications

References 8 publications

Triaxial Test and Discrete Element Numerical Simulation of Geogrid-Reinforced Clay Soil

Triaxial Test and Discrete Element Numerical Simulation of Geogrid-Reinforced Clay Soil

Machine Learning Models to Generate a Subsurface Soil Profile: A Case of Makati City, Philippines

Development of a k-Nearest Neighbor (kNN) Machine Learning Model to Estimate the SPT N-Values of Valenzuela City, Philippines

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