511
Views
0
CrossRef citations to date
0
Altmetric
Original Articles

Complexity of frictional interfaces: a complex network perspective

, , &
Pages 167-178 | Received 24 Nov 2011, Accepted 31 Aug 2012, Published online: 15 Oct 2012
 

Abstract

The shear strength and stick-slip behaviour of a rough rock joint are analysed using the complex network approach. We develop a network approach using correlation patterns of void spaces of an evolvable rough fracture (crack type II). Correlation among networks' properties with the hydro-mechanical attributes (obtained from experimental tests) of fracture before and after slip is the direct result of the revealed non-contacts networks. We show that networks' parameters yield a close relation to the contact zones' attachment-detachment sequences through the evolution of frictional interfaces. Furthermore results showed correlated patterns of sheared interfaces demonstrating assortative networks indicating the role of ‘hubs’ in driving frictional interfaces. Also, we discuss the scaling of fraction of ‘loops’ in formed networks with different stages of shear strength evolution. Our method can be developed to investigate the complexity of stick-slip behaviour of faults as well as new interpretations of friction laws in terms of network parameters.

Acknowledgements

The first author would like to acknowledge and thank Mrs J. Youssefian, who shared her comments and suggestions during the preparation of the manuscript.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 203.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.