Reassessing the Joint Roughness Coefficient (JRC) Estimation Using Z2

By Zon-Yee Yang, S. Lo, C. Di. 2001

233 citations recorded by the source corpus.

Subfields: Computational Mechanics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/1ee0b74d90f0af326475165fe30d32e3fb11558a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Yanrong Li
2. Run-qiu Huang

In Mechanical Engineering

1. Yanrong Li
2. Run-qiu Huang

Citations in the corpus

• A new method estimating the 2D Joint Roughness Coefficient for discontinuity surfaces in rock masses (2014, methodology): “[20] proved that, due to the selfaffinity transformation law, scaling up and down of the 10 standard JRC profiles should not influence the final JRC values.”
• Estimation of the joint roughness coefficient (JRC) of rock joints by vector similarity measures (2017, methodology): “…visual comparison method, several algorithms supported by the parameters derived from digitized standard profiles have been presented for automated JRC calculations (Tse and Cruden 1979; Maerz et al. 1990; Yu and Vayssade 1991; Yang et al. 2001a, b; Tatone and Grasselli 2010; Zhang et al. 2014).”
• Quantitative estimation of joint roughness coefficient using statistical parameters (2015, background): “[10] to be sensitive to the sampling interval.”
• New Peak Shear Strength Criterion of Rock Joints Based on Quantified Surface Description (2014, background): “…proposed by Grasselli and his colleagues can be considered as the best roughness evaluation technique, since the surface is assessed according to the exact geometrical shape of asperities and the actual potential contact areas, without any average value of surface variables (Yang et al. 2011).”
• Relationship between joint roughness coefficient and fractal dimension of rock fracture surfaces (2015, methodology): “The development of objective methods was gradually advanced by researchers considering statistical parameters and the fractal dimension of the rock joint profiles [4–8].”