A novel approach to structural anisotropy classification for jointed rock masses using theoretical rock quality designation formulation adjusted to joint spacing

Rock quality designation(RQD)has been considered as a one-dimensional jointing degree property since it should be determined by measuring the core lengths obtained from drilling.Anisotropy index of jointing degree(AI_(jd))was formulated by Zheng et al.(2018)by considering maximum and minimum values of RQD for a jointed rock medium in three-dimensional space.In accordance with spacing terminology by ISRM(1981),defining the jointing degree for the rock masses composed of extremely closely spaced joints as well as for the rock masses including widely to extremely widely spaced joints is practically impossible because of the use of 10 cm as a threshold value in the conventional form of RQD.To overcome this limitation,theoretical RQD(TRQD_(t))introduced by Priest and Hudson(1976)can be taken into consideration only when the statistical distribution of discontinuity spacing has a negative exponential distribution.Anisotropy index of the jointing degree was improved using TRQD_(t) which was adjusted to wider joint spacing by considering Priest(1993)’s recommendation on the use of variable threshold value(t)in TRQD_(t) formulation.After applications of the improved anisotropy index of a jointing degree(AI'_(jd))to hypothetical jointed rock mass cases,the effect of persistency of joints on structural anisotropy of rock mass was introduced to the improved AI'_(jd) formulation by considering the ratings of persistency of joints as proposed by Bieniawski(1989)’s rock mass rating(RMR)classification.Two real cases were assessed in the stratified marl and the columnar basalt using the weighted anisotropy index of jointing degree(W_AI'_(jd)).A structural anisotropy classification was developed using the RQD classification proposed by Deere(1963).The proposed methodology is capable of defining the structural anisotropy of a rock mass including joint pattern from extremely closely to extremely widely spaced joints.

Application of Chebyshev theorem to data preparation in landslide susceptibility mapping studies:an example from Yenice(Karabük,Turkey)region

Landslide database construction is one of the most crucial stages of the landslide susceptibility mapping studies.Although there are many techniques for preparing landslide database in the literature,representative data selection from huge data sets is a challenging,and,to some extent,a subjective task.Thus,in order to produce reliable landslide susceptibility maps,data-driven,objective and representative database construction is a very important stage for these maps.This study mainly focuses on a landslide database construction task.In this study,it was aimed at building a representative landslide database extraction approach by using Chebyshev theorem to evaluate landslide susceptibility in a landslide prone area in the WesternBlack Sea region of Turkey.The study area was divided into two different parts such as training(Basin 1) and testing areas(Basin 2).A total of nine parameters such as topographical elevation,slope,aspect,planar and profile curvatures,stream power index,distance to drainage,normalized difference vegetation index and topographical wetness index were used in the study.Next,frequency distributions of the considered parameters in both landslide and nonlandslide areas were extracted using different sampling strategies,and a total of nine different landslide databases were obtained.Of these,eight databases were gathered by the methodology proposed by this study based on different standard deviations and algebraic multiplication of raster parameter maps.To evaluate landslide susceptibility,Artificial Neural Network method was used in thestudy area considering the different landslide and nonlandslide data.Finally,to assess the performance of the so-produced landslide susceptibility map based on nine data sets,Area Under Curve(AUC approach was implemented both in Basin 1 and Basin2.The best performances(the greatest AUC values were gathered by the landslide susceptibility map produced by two standard deviation databas extracted by the Chebyshev theorem,as 0.873 and0.761,respectively.Results revealed that th methodology proposed by this study is a powerful and objective approach in landslide susceptibility mapping.