A new internal friction angle-based approach for estimating Hoek-Brown constant m(i) and its comparison with those estimated from some current methods

Karakul H., ULUSAY R.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, vol.81, no.8, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 81 Issue: 8
  • Publication Date: 2022
  • Doi Number: 10.1007/s10064-022-02820-x
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, IBZ Online, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Keywords: Hoek-Brown material constant, Uniaxial compressive strength, Tensile strength, Internal friction angle, R-index, Guideline chart, TENSILE-STRENGTH, ROCK, CRITERION, DILATION, BEHAVIOR, GRANITE, STRAIN, MODEL
  • Hacettepe University Affiliated: Yes


One of the two independent key parameters of the Hoek-Brown failure criterion, m(i), is calculated using 3-axial test data consisting of at least five major and minor principal stresses and the regression method. In the absence of 3-axial test data, the guideline chart, which provides m(i) values, is commonly used in practice. However, m(i) values calculated from regression analyses have a much greater spread than that proposed in the Guideline. Therefore, simplified models, based on some strength properties, have been proposed to calculate m(i) values. But some of these prediction models were based on a few types of rock and limited data. In this study, an alternative method for predicting m(i) using internal friction angle (phi), which is determined from classical 3-axial test, is proposed. First a brief description of the proposed method was given and a summary on the existing methods for predicting m(i) was discussed with their limitations, and the data selection criteria used by the authors were also described. In order to assess the prediction performance of the proposed method, a database, consisting of 125 data sets and representing a total of 26 different rock types, was compiled from a well-known dataset and published sources. Results of the comparisons between the proposed method and some of the current prediction methods mainly suggested that the proposed method yields the best m(i) predictions. It is also noted that for sedimentary rocks, the ranges of m(i) estimated from this method were closer to those proposed in the Guideline chart.