ArticleScientific reports2025
Assessment of compressive strength of eco-concrete reinforced using machine learning tools.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
6 citing papers in PubMed.
- White-box soft computing models for predicting the strength of sustainable pozzolanic concretes.Scientific reports · 2026Article
- Article
- Predicting compressive strength of mortars containing recycled CRT glass using GMDH and GEP methods.Scientific reports · 2026Article
- Machine learning models with SHAP for performance prediction of eco-friendly fiber reinforced mortars with glass waste.Scientific reports · 2025Article
- Diagnosis of misalignment faults using the DTCWT-RCMFDE and LSSVM algorithms.Scientific reports · 2025Article
- Enhancing PV power forecasting through feature selection and artificial neural networks: a case study.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
10 authors.
Funding
Abstract
Predicting the compressive strength of Compressed Earth Blocks (CEB) is a challenging task due to the nonlinear relationships among their diverse components, including cement, clay, sand, silt, and fibers. This study employed PyCaret, an automated machine learning platform, to address this complexity by developing and evaluating predictive models. The analysis demonstrated that fiber content exhibited a strong positive correlation with cement content, with a correlation coefficient of 0.9444, indicating a significant influence on compressive strength. Multiple machine learning algorithms were tested using metrics such as the coefficient of determination (R
Indexed as
Identifiers
What OpenQuestion holds
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.