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

Bawar IftikharDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, PakistanSophia C. AlihInstitute of Noise and Vibration, School of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, MalaysiaMohammadreza VafaeiSchool of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, MalaysiaMuhammad Faisal JavedDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, PakistanMuhammad Faisal RehmanDepartment of Architecture, University of Engineering and Technology Peshawar, Abbottabad Campus, Abbottabad, PakistanSherzod AbdullaevDepartment of Science and Innovation, Tashkent State Pedagogical University Named after Nizami, Bunyodkor Street 27, Tashkent, UzbekistanNissren TamamDepartment of Physics, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi ArabiaM. Ijaz KhanDepartment of Mathematics and Statistics, Riphah International University, I-14, Islamabad, 44000, Pakistan. [email protected]Ahmed M. HassanCenter of Research, Faculty of Engineering, Future University in Egypt, New Cairo, 11835, Egypt

Scientific Reportsjournal2023en

ABI

Annotatsiya

Abstract 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 R 2 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 R 2 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.

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DOI: 10.1038/s41598-023-39349-2

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