Publication detail

Joint Rock Coefficient Estimation Based on Hausdorff Dimension

MARTIŠEK, D.

English title

Joint Rock Coefficient Estimation Based on Hausdorff Dimension

Type

journal article - other

Language

en

Original abstract

The strength of rock structures strongly depends inter alia on surface irregularities of rock joints. These irregularities are characterized by a coefficient of joint roughness. For its estimation, visual comparison is often used. This is rather a subjective method, therefore, fully computerized image recognitionprocedures were proposed. However, many of them contain imperfections, some of them even mathematical nonsenses and their application can be very dangerous in technical practice. In this paper, we recommend mathematically correct method of fully automatic estimation of the joint roughness coefficient. This method requires only the Barton profiles as a standard.

English abstract

The strength of rock structures strongly depends inter alia on surface irregularities of rock joints. These irregularities are characterized by a coefficient of joint roughness. For its estimation, visual comparison is often used. This is rather a subjective method, therefore, fully computerized image recognitionprocedures were proposed. However, many of them contain imperfections, some of them even mathematical nonsenses and their application can be very dangerous in technical practice. In this paper, we recommend mathematically correct method of fully automatic estimation of the joint roughness coefficient. This method requires only the Barton profiles as a standard.

Keywords in English

Hausdorff Dimension, Self-Similarity, Self-Affinity, Box Counting Method, Power Function Method, Barton Profile, JRC Index

Released

18.12.2017

Publisher

Scientific Research Publishing

Location

USA

ISSN

2160-0368

Volume

7

Number

11

Pages from–to

615–640

Pages count

26

BIBTEX


@article{BUT147476,
  author="Dalibor {Martišek},
  title="Joint Rock Coefficient Estimation Based on Hausdorff Dimension",
  year="2017",
  volume="7",
  number="11",
  month="December",
  pages="615--640",
  publisher="Scientific Research Publishing",
  address="USA",
  issn="2160-0368"
}