2018
DOI: 10.1007/s00603-018-1531-0
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3D Particle-Based DEM Investigation into the Shear Behaviour of Incipient Rock Joints with Various Geometries of Rock Bridges

Abstract: A 3D particle-based DEM model was established taking into account the geometries of rock bridges. The model was used to investigate the shear behaviour of incipient rock joints. Fifty-seven direct shear tests were conducted under constant normal load (CNL) boundary conditions using the established model, in which rock bridges with nineteen different geometries and incipient joints with various areal persistence (between 0.2 and 0.96) were involved. Our results show that, for the cases having a single rock brid… Show more

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Cited by 40 publications
(14 citation statements)
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“…Without distinguishing the difference between specimens, the shear strength parameters obtained by directly fitting the data can neither faithfully reflect the effect of normal stress on the shear strength nor can faithfully reflect the shear strength of a group of specimens even if the value of R 2 is high. Nevertheless, shear test under varying normal stresses provides a considerable amount of useful information that should be fully analyzed and applied [14,15]. As such, developing a suitable analysis method to obtain shear strength parameters that can better re ect the shear characteristics of the rock mass joint surface is of paramount importance.…”
Section: Conventional Multiple-specimen Methods Formentioning
confidence: 99%
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“…Without distinguishing the difference between specimens, the shear strength parameters obtained by directly fitting the data can neither faithfully reflect the effect of normal stress on the shear strength nor can faithfully reflect the shear strength of a group of specimens even if the value of R 2 is high. Nevertheless, shear test under varying normal stresses provides a considerable amount of useful information that should be fully analyzed and applied [14,15]. As such, developing a suitable analysis method to obtain shear strength parameters that can better re ect the shear characteristics of the rock mass joint surface is of paramount importance.…”
Section: Conventional Multiple-specimen Methods Formentioning
confidence: 99%
“…Moreover, regardless of whether shear test is conducted with natural joint specimens collected on-site [3][4][5][6] or artificially prepared, joint specimens [7][8][9][10][11][12][13] have been widely studied, and it is impossible to ensure that all specimens have the same morphological feature on the joint surface. For example, by the study of Shang et al [14], even for some rock joints with the same areal persistence, the measured shear strength is still different. At the same time, Ji et al [15] found it is different to remove the effect of surface irregularity from the test results.…”
Section: Introductionmentioning
confidence: 98%
“…Many investigations have been undertaken to measure the shear strength of discontinuities, mostly focusing on mechanical discontinuities with zero true cohesion (Barton, 1976). For non-filled and non-persistent rock joints, shear strength is however controlled by four components including fundamental shear strength of rock bridges (Shang and Zhao, 2017), internal friction in solid bridges (after rock bridges are mobilized), friction from the persistent joint segments (Lajtai, 1969b) as well as geometry and location of bridges (Ghazvinian et al, 2007). An equivalent shear strength calculation method was developed based on the Mohr-Coulomb failure criterion, in which strength contributions from rock bridges and persistent joint areas are linearly combined (Lajtai, 1969a;Hudson and Harrison, 2000) as expressed by the following equation:…”
Section: Mechanical Properties Of Individual Discontinuitiesmentioning
confidence: 99%
“…Rock bridges significantly increase the shear strength of individual incipient rock discontinuities (Shang and Zhao. 2017), especially under constant normal stiffness boundary conditions (Shang et al, 2018).…”
Section: Mechanical Properties Of Individual Discontinuitiesmentioning
confidence: 99%
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