Detail publikace

Joint Rock Coefficient Estimation Based on Hausdorff Dimension

MARTIŠEK, D.

Anglický název

Joint Rock Coefficient Estimation Based on Hausdorff Dimension

Typ

článek v časopise - ostatní, Jost

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova anglicky

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

Vydáno

18.12.2017

Nakladatel

Scientific Research Publishing

Místo

USA

ISSN

2160-0368

Ročník

7

Číslo

11

Strany od–do

615–640

Počet stran

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"
}