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Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming

Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressiv...

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Main Authors: Iftikhar, Bawar, Alih, Sophia C., Vafaei, Mohammadreza, Javed, Muhammad Faisal, Rehman, Muhammad Faisal, Abdullaev, Sherzod Shukhratovich, Tamam, Nissren, Khan, M. Ijaz, Hassan, Ahmed M.
Format: Article
Language:English
Published: Springer Nature 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/106850/1/BawarIftikhar2023_PredictingCompressiveStrengthofEcoFriendly.pdf
http://eprints.utm.my/106850/
http://dx.doi.org/10.1038/s41598-023-39349-2
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spelling my.utm.1068502024-08-01T05:33:07Z http://eprints.utm.my/106850/ Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming Iftikhar, Bawar Alih, Sophia C. Vafaei, Mohammadreza Javed, Muhammad Faisal Rehman, Muhammad Faisal Abdullaev, Sherzod Shukhratovich Tamam, Nissren Khan, M. Ijaz Hassan, Ahmed M. TA Engineering (General). Civil engineering (General) Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressive strength of these blocks. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) to develop empirical models to forecast the compressive strength of plastic sand paver blocks (PSPB) comprised of plastic, sand, and fibre in an effort to advance the field. The database contains 135 results for compressive strength with seven input parameters. The R2 values of 0.87 for GEP and 0.91 for MEP for compressive strength reveal a relatively significant relationship between predicted and actual values. MEP outperformed GEP by displaying a higher R2 and lower values for statistical evaluations. In addition, a sensitivity analysis was conducted, which revealed that the sand grain size and percentage of fibres play an essential part in compressive strength. It was estimated that they contributed almost 50% of the total. The outcomes of this research have the potential to promote the reuse of PSPB in the building of green environments, hence boosting environmental protection and economic advantage. Springer Nature 2023-07-27 Article PeerReviewed application/pdf en http://eprints.utm.my/106850/1/BawarIftikhar2023_PredictingCompressiveStrengthofEcoFriendly.pdf Iftikhar, Bawar and Alih, Sophia C. and Vafaei, Mohammadreza and Javed, Muhammad Faisal and Rehman, Muhammad Faisal and Abdullaev, Sherzod Shukhratovich and Tamam, Nissren and Khan, M. Ijaz and Hassan, Ahmed M. (2023) Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming. Scientific Reports, 13 (1). pp. 1-17. ISSN 2045-2322 http://dx.doi.org/10.1038/s41598-023-39349-2 DOI:10.1038/s41598-023-39349-2
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Iftikhar, Bawar
Alih, Sophia C.
Vafaei, Mohammadreza
Javed, Muhammad Faisal
Rehman, Muhammad Faisal
Abdullaev, Sherzod Shukhratovich
Tamam, Nissren
Khan, M. Ijaz
Hassan, Ahmed M.
Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming
description Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressive strength of these blocks. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) to develop empirical models to forecast the compressive strength of plastic sand paver blocks (PSPB) comprised of plastic, sand, and fibre in an effort to advance the field. The database contains 135 results for compressive strength with seven input parameters. The R2 values of 0.87 for GEP and 0.91 for MEP for compressive strength reveal a relatively significant relationship between predicted and actual values. MEP outperformed GEP by displaying a higher R2 and lower values for statistical evaluations. In addition, a sensitivity analysis was conducted, which revealed that the sand grain size and percentage of fibres play an essential part in compressive strength. It was estimated that they contributed almost 50% of the total. The outcomes of this research have the potential to promote the reuse of PSPB in the building of green environments, hence boosting environmental protection and economic advantage.
format Article
author Iftikhar, Bawar
Alih, Sophia C.
Vafaei, Mohammadreza
Javed, Muhammad Faisal
Rehman, Muhammad Faisal
Abdullaev, Sherzod Shukhratovich
Tamam, Nissren
Khan, M. Ijaz
Hassan, Ahmed M.
author_facet Iftikhar, Bawar
Alih, Sophia C.
Vafaei, Mohammadreza
Javed, Muhammad Faisal
Rehman, Muhammad Faisal
Abdullaev, Sherzod Shukhratovich
Tamam, Nissren
Khan, M. Ijaz
Hassan, Ahmed M.
author_sort Iftikhar, Bawar
title Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming
title_short Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming
title_full Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming
title_fullStr Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming
title_full_unstemmed Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming
title_sort predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming
publisher Springer Nature
publishDate 2023
url http://eprints.utm.my/106850/1/BawarIftikhar2023_PredictingCompressiveStrengthofEcoFriendly.pdf
http://eprints.utm.my/106850/
http://dx.doi.org/10.1038/s41598-023-39349-2
_version_ 1806442417822367744
score 13.211869

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