Softening in random networks of non-identical beams

Ehsan Ban, Victor H. Barocas, Mark S. Shephard, R. Catalin Picu

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Random fiber networks are assemblies of elastic elements connected in random configurations. They are used as models for a broad range of fibrous materials including biopolymer gels and synthetic nonwovens. Although the mechanics of networks made from the same type of fibers has been studied extensively, the behavior of composite systems of fibers with different properties has received less attention. In this work we numerically and theoretically study random networks of beams and springs of different mechanical properties. We observe that the overall network stiffness decreases on average as the variability of fiber stiffness increases, at constant mean fiber stiffness. Numerical results and analytical arguments show that for small variabilities in fiber stiffness the amount of network softening scales linearly with the variance of the fiber stiffness distribution. This result holds for any beam structure and is expected to apply to a broad range of materials including cellular solids.

Original languageEnglish (US)
Pages (from-to)38-50
Number of pages13
JournalJournal of the Mechanics and Physics of Solids
Volume87
DOIs
StatePublished - Feb 1 2016

Bibliographical note

Funding Information:
E.B. is grateful to Javad Heydari and Abouzar Ghavami at RPI for fruitful discussions and also Dan Fovargue, Lijuan Zhang and Ali Shahsavari at RPI for their help with the finite element simulations. We also gratefully acknowledge the financial support of the National Institute of Health (Grant nos. RO1-EB005813 and U01-EB016638 ).

Publisher Copyright:
© 2015 Elsevier Ltd. All rights reserved.

Keywords

  • Beam Structures
  • Elastic Materials
  • Heterogeneous Materials
  • Microstructures
  • Probability and Statistics

Fingerprint

Dive into the research topics of 'Softening in random networks of non-identical beams'. Together they form a unique fingerprint.

Cite this