<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>A promising substitute for regular concrete is geopolymer concrete. Engineering mechanical parameters of geopolymer concrete, including compressive strength, are frequently measured in the laboratory or in-situ via experimental destructive tests, which calls for a significant quantity of raw materials, a longer time to prepare the samples, and expensive machinery. Thus, to evaluate compressive strength, non-destructive testing is preferred. Therefore, the objective of this research is to develop an artificial neural network model based on the results of destructive and non-destructive tests to assess the compressive strength of geopolymer concrete without needing further destructive tests. According to the artificial neural network analysis developed in this study, the compressive strength of geopolymer concrete can be predicted rather accurately by combining the results of the non-destructive with <italic>R</italic><sup>2</sup> of 0.9286.</p>
</abstract>

A promising substitute for regular concrete is geopolymer concrete. Engineering mechanical parameters of geopolymer concrete, including compressive strength, are frequently measured in the laboratory or in-situ via experimental destructive tests, which calls for a significant quantity of raw materials, a longer time to prepare the samples, and expensive machinery. Thus, to evaluate compressive strength, non-destructive testing is preferred. Therefore, the objective of this research is to develop an artificial neural network model based on the results of destructive and non-destructive tests to assess the compressive strength of geopolymer concrete without needing further destructive tests. According to the artificial neural network analysis developed in this study, the compressive strength of geopolymer concrete can be predicted rather accurately by combining the results of the non-destructive with R2 of 0.9286.

Prediction of Geopolymer Concrete Compressive Strength Utilizing Artificial Neural Network and Nondestructive Testing

Civil and Environmental Engineering

This work is licensed under the Creative Commons Attribution 4.0 International License.

A promising substitute for regular concrete is geopolymer concrete. Engineering mechanical parameters of geopolymer concrete, including compressive strength,...

Prediction of Geopolymer Concrete Compressive Strength Utilizing Artificial Neural Network and Nondestructive Testing

Data publikacji: 14 gru 2022

Zakres stron: 655 - 665

DOI: https://doi.org/10.2478/cee-2022-0060

Słowa kluczowe
Artificial neural network, Geopolymer concrete, Nondestructive test, Ultrasonic pulse velocity, Schmidt rebound hammer

© 2022 Hatem Almasaeid et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Prediction of Geopolymer Concrete Compressive Strength Utilizing Artificial Neural Network and Nondestructive Testing

Data publikacji: 14 gru 2022

Zakres stron: 655 - 665

DOI: https://doi.org/10.2478/cee-2022-0060

Słowa kluczoweArtificial neural network, Geopolymer concrete, Nondestructive test, Ultrasonic pulse velocity, Schmidt rebound hammer

© 2022 Hatem Almasaeid et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Artificial neural network, Geopolymer concrete, Nondestructive test, Ultrasonic pulse velocity, Schmidt rebound hammer