ArticlePolymers2025
Compressive Strength Prediction of Green Concrete with Recycled Glass-Fiber-Reinforced Polymers Using a Machine Learning Approach.
Article in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- From Waste to Value-Added Resource: A Strategic Review of Recycling and Regeneration Pathways for Fiber-Reinforced Polymer Waste.Polymers · 2026Review
- AI-driven sustainable strength prediction and experimental evaluation of high-performance fiber-reinforced concrete incorporating metakaolin.Scientific reports · 2026Article
- An Interpretable Ensemble Machine Learning Framework for Predicting the Ultimate Flexural Capacity of BFRP-Reinforced Concrete Beams.Polymers · 2026Article
- Explainable machine learning model and gene expression programming for predicting reinforced concrete beams moment capacity exposed to fire.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fiber-reinforced polymer (FRP) materials are increasingly used in the construction and transportation industries, generating growing volumes of waste. This study applied a machine learning model to predict the compressive strength of eco-friendly concrete incorporating recycled glass-fiber-reinforced polymer (GFRP) waste. Based on 119 laboratory mixes, the model achieved a good prediction accuracy (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.