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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
    • MAHSA University
    • Islamic Azad University (Damavand Branch)
    • University Technology Malaysia

    Research output: Contribution to journalArticlepeer-review

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    Abstract

    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
    Volume22
    Early online date28 Dec 2019
    DOIs
    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/

    Funding

    The authors sincerely wish to express their profound gratitude to the Research Management Centre (RMC) at Universiti Teknologi Malaysia for their financial support of this research. This study was funded under the grant number of QJ130000252206H52.

    Keywords

    • 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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