Investigation on stability of soil–rock mixture slope with discrete element method

Yu, Jun; Zhang, Qiang; Wu, Changjiang; Jia, Chaojun

doi:10.1007/s12665-023-11107-7

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…The calculation method for normal elastic force is provided in Formulae ( 4) and ( 5). The definitions of the equivalent elastic modulus E* and the equivalent contact radius R* are shown in Formulas ( 6) and (7).…”

Section: Selection Of the Contact Modelmentioning

confidence: 99%

“…Due to the complex soil characteristics, the finite element soil model is not very accurate, and can only simulate soil damage behavior, but cannot simulate the soil movement process. The discrete element method can solve the contact between particles and boundaries and uses adhesive particles to simulate the generation of soil aggregates, greatly improving the accuracy of soil models [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Calibration and Verification of Discrete Element Parameters of Surface Soil in Camellia Oleifera Forest

Ma,

You,

Yang

et al. 2024

Agronomy

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To analyze the interaction between the surface soil and the soil-contacting component (65 Mn) in the camellia oleifera forest planting area in Changsha City, Hunan, China, in this study, we conducted discrete element calibration using physical and simulation tests. The chosen contact model was Hertz–Mindlin with JKR cohesion, with the soil repose angle as the response variable. The repose angle of the soil was determined to be 36.03° based on the physical tests. The significant influencing factors of the repose angle determined based on the Plackett–Burman test were the soil–soil recovery coefficient, soil–soil rolling friction coefficient, soil-65 Mn static friction coefficient, and surface energy of soil for the JKR model. A regression model for the repose angle was developed using the Box–Behnken response surface optimization method to identify the best parameter combination. The optimal parameter combination for the JKR model was determined as follows: surface energy of soil: 0.400, soil–soil rolling friction coefficient: 0.040, soil-65 Mn static friction coefficient: 0.404, and soil–soil recovery coefficient: 0.522. The calibrated discrete element parameters were validated through experiments on the repose angle and steel rod insertion. The results indicated that the relative errors obtained from the two verification methods were 2.44% and 1.71%, respectively. This research offers fundamental insights for understanding the interaction between soil and soil-contacting components and optimizing their design.

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Section: Selection Of the Contact Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%