Unit composite friction coefficient of model pile floated in kaolin clay reinforced by recycled crushed glass under uplift loading

Shin To Amiri, Ali Dehghanbanadaki, Ramli Nazir, Shervin Motamedi

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
89 Downloads (Pure)


In this study, increment in the shear strength of soft cohesive soil (Kaolinite S300) that have been mixed with 10–50% crushed, curbside collected, glass was evaluated using a set of physical modeling tests. The selected crushed glass for this study i.e. those filtered through the 2.36 mm (#8) sieve and retained at 1.18 mm (#16) was collected from Johor Bahru, Malaysia. Floated piles (from 10 mm to 50 mm diameter) for pull-out test conditions were examined to investigate the effect of Crushed Glass-Kaolinite (CG-K) mixture on skin resistance strength of piles. Moreover, the new unit composite friction coefficient “κ” has been introduced to utilize in skin resistant capacity of piles. In addition, to predict the κ, a Multi-Layer Perceptron model (MLP) and a Radial Basis Function (RBF) were employed and the result shows that the experimental data has been fitted with good accuracy using the obtained models. The final results indicated that the κ increased with the percentage of Crushed Glass increment. For example, for 10 mm diameter pile floated in pure kaolinite, the ultimate skin resistance capacity was 63.55 N whereas, with addition of 50% crushed glass with Kaolinite, the ultimate capacity was increased to 132.25 N. Finally, the results of computations showed that the trained MLP and RBF model proposed in this study is capable of accurate prediction of κ despite the complexity arises from the non-linearity of the problem.

Original languageEnglish
Article number100313
JournalTransportation Geotechnics
Early online date28 Dec 2019
Publication statusPublished - 1 Mar 2020

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Transportation Geotechnics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Transportation Geotechnics, 22, (2020) DOI: 10.1016/j.trgeo.2019.100313

© 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/


  • Artificial neural network
  • Crushed glass
  • Pile
  • Skin resistance
  • Soft soil
  • Waste material

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation
  • Geotechnical Engineering and Engineering Geology


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