Neural Computing and Applications

, Volume 21, Issue 3, pp 441–452 | Cite as

Triaxial compression behavior of sand and tire wastes using neural networks

  • Ayse Edincliler
  • Ali Firat Cabalar
  • Ahmet Cagatay
  • Abdulkadir Cevik
Original Article


Tire waste additions to sand enhance the shear strength of sand for embankments. Granular and fiber shape tire wastes and their mixture with sand under drained and undrained conditions were tested in triaxial compression apparatus and modeled using neural networks (NN). In the experimental study, tire crumb and tire buffings inclusions were used at varying contents as soil reinforcement. Both quick tests and consolidated drained (CD) triaxial tests were performed to analyze the effects of tire content, tire shape, and tire aspect ratio on the shear strength of sand. Then, this extensive experimental database obtained in laboratory was used in training, testing, and prediction phases of three neural network-based soil models. The input variables in the developed NN models are tire wastes content, tire wastes type, test type, effective stress, and axial strain, and the output is the deviatoric stress. The accuracy of proposed models seems to be satisfactory. Furthermore, the proposed models are also presented as simple explicit mathematical functions for further use by researchers.


Triaxial compression test Sand Tire wastes Shear strength Neural networks 


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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Ayse Edincliler
    • 1
  • Ali Firat Cabalar
    • 2
  • Ahmet Cagatay
    • 3
  • Abdulkadir Cevik
    • 2
  1. 1.Department of Earthquake EngineeringBogazici UniversityIstanbulTurkey
  2. 2.Department of Civil EngineeringUniversity of GaziantepGaziantepTurkey
  3. 3.Department of Civil EngineeringBogazici UniversityIstanbulTurkey

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