Yunfeng Ge

TL;DR: In this article, a comprehensive review on rock joint roughness measurement and quantification procedures is provided, which shows the capability of fractal based methods in accurate quantification of natural rock joints roughness.

TL;DR: In this article, the authors proposed a new roughness index (λ) using a Root Mean Square method, which considers inclination angle, amplitude of asperities and their directions, and derived the logistics function between λ and JRC.

TL;DR: The bank destruction forecasting study for accumulative landslides in the Three Gorges Reservoir Region, China utilizing back-propagation (BP) neural network approach found that the BP neural network model had obvious advantages over the convention approaches in the aspects of the fast calculation speed and high convenience.

TL;DR: Wang et al. as mentioned in this paper proposed a shadow area percentage (SAP) based approach to quantify surface roughness, and compared with the result from direct shear testing and based on statistics, an empirical formula was found among SAP, normal stress, and shear strength.

TL;DR: A new index enabling to capture all the three features of roughness, namely brightness area percentage (BAP), is presented to express the roughness based on synthetic illumination of a digital terrain model derived from terrestrial laser scanner (TLS).