Results in Engineering (Mar 2024)

Mechanical and environmental evaluation of PET plastic-graphene nano platelets concrete mixes for sustainable construction

  • Muhammad Usman Ghani,
  • Bing Sun,
  • Moustafa Houda,
  • Sheng Zeng,
  • Muhammad Basit khan,
  • Hany M.Seif ElDin,
  • Ahsan Waqar,
  • Omrane Benjeddou

Journal volume & issue
Vol. 21
p. 101825

Abstract

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In recent times, there has been a substantial increase in the annual use of plastics, resulting in a major upward trend. As a result, recycling plastic waste has become a major global issue. The present research aims to explore the feasibility of utilizing Polyethylene terephthalate (PET) as a possible substitute for coarse aggregate in concrete. In addition, the integration of Graphene Nano-platelets (GNPs) into the concrete mixture was conducted using different quantities, aiming to improve its structural integrity. The study employed an experimental research design to carry out its examination. PET was included into concrete at varied ratios, namely 0 %, 5 %, 15 %, 20 %, and 25 %, serving as an additional ingredient alongside coarse aggregate. Additionally, GNPs were introduced into the mixture at various weight percentages, namely 0 %, 0.03 %, 0.05 %, 0.08 %, and 0.1 %. A series of mechanical tests were performed to evaluate various properties of the concrete, including compressive strength (CS), split tensile strength (STS), flexural strength (FS), modulus of elasticity (MoE), ultrasonic pulse velocity (UPV). In addition, an assessment of the concrete's environmental impact was conducted by analysing the carbon content and evaluating its eco-efficiency (ESE). The research study revealed that the incorporation of 5 % PET as a replacement for coarse aggregate, together with the inclusion of 0.1 % GNPs, resulted in the optimal enhancement of CS, STS, FS, MOE, UPV by 9 %, 12.21 %, 4.40 %, 4.40 % and 0.070 % respectively. The Response Surface Methodology (RSM) models were developed, and mathematical equations were generated in order to predict the expected results. The optimization process for all the models was carried out using a multi-objective optimization technique, followed by a subsequent validation process.

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