Vol. 20 No. 3 (2023): Journal of Non Destructive Testing and Evaluation (JNDE), Sept 2023
Research Papers

Assessment of residual compressive strength of geopolymer concrete exposed to elevated temperature using Artificial Neural Network approach

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  • Kishor S. Kulkarni,
  • Sana D. Sayyad,
  • M. Reyazur Rahman
Kishor S. Kulkarni
CSIR-Central Building Research Institute, Roorkee

Published 10-09-2023

Keywords

  • Geopolymer concrete,
  • elevated temperature,
  • compressive strength,
  • artificial neural network

How to Cite

Kishor S. Kulkarni, Sana D. Sayyad, & M. Reyazur Rahman. (2023). Assessment of residual compressive strength of geopolymer concrete exposed to elevated temperature using Artificial Neural Network approach. Journal of Non-Destructive Testing and Evaluation (JNDE), 20(3), 50–57. Retrieved from https://jnde.isnt.in/index.php/JNDE/article/view/47

Copyright (c) 2023 Journal of Non-Destructive Testing and Evaluation (JNDE)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The population growth and industrial activities nowadays creates a considerable volume of rubbish, producing disposal challenges and major environmental hazards. The cement industry is a major generator of greenhouse gases like carbon dioxide. The use of waste resources, which avoids disposal worries while lowering greenhouse gases emissions into the atmosphere. This is a key factor in the development of cement-free Geopolymer Concrete (GPC). In the present work, GPC was made using a 75:25 proportions of fly ash (FA) and ground granulated blast furnace slag (GGFBS). The cube specimens were exposed to various temperatures ranging from 200 °C, 400°C, 600°C and 800 °C. The mechanical characteristics were then assessed. The compressive strength of GPC exposed to elevated temperatures is predicted using an Artificial Neural Network (ANN) technique in this study. The assessment of the residual compressive strength of GPC exposed to elevated temperatures was also done using non-destructive tests (NDT), such as the Rebound Hammer (RH) and Ultrasonic Pulse Velocity (UPV). In this study, the 255 experimental data from the other studies are also used for the ANN model's training, testing, and verification. The results of the study validate the use of ANN models to estimate the concrete's residual compressive strength in a manner that is quite similar to experimental data. The results show that the average prediction error for an unknown set of data was about 3.5 MPa.

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