ArticleScientific reports2023
Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Geotechnical improvement of dune sand embankments using rubber crumb and brick powder: RSM vs. ANN-GA hybrid optimization.Scientific reports · 2025Article
- Use of BOIvy Optimization Algorithm-Based Machine Learning Models in Predicting the Compressive Strength of Bentonite Plastic Concrete.Materials (Basel, Switzerland) · 2025Article
- Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches.Scientific reports · 2024Article
- Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite.PloS one · 2024Article
- Soft computing techniques for predicting the properties of raw rice husk concrete bricks using regression-based machine learning approaches.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.