ArticleHeliyon2023
Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- An experimental and computational intelligence simulation for predicting the strength accuracy of self-compacting concrete using Linz-Donawitz Slag as industrial waste derivatives.Scientific reports · 2026Article
- Prediction of crack repair percentage in self-healing concrete using machine learning.Scientific reports · 2025Article
- Experimental and machine learning prediction of compressive strength of chemically activated RHA based RAC using SHAP and PDP analysis.Scientific reports · 2025Article
- Comparative Study of Machine Learning for Predicting Compressive Strength in Oyster Shell Cementitious Composites.Materials (Basel, Switzerland) · 2025Article
- Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete.Scientific reports · 2025Article
- Machine learning predictions of high-strength RCA concrete utilizing chemically activated fly ash and nano-silica.Scientific reports · 2025Article
- Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis.Scientific reports · 2025Article
- A comparative study of various statistical and machine learning models for predicting restaurant demand in Bangladesh.PloS one · 2025Article
- Article
- Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP.Scientific reports · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Construction industry is indirectly the largest source of
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.