ArticleScientific reports2025
Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO
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 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.
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Who cites it
4 citing papers in PubMed.
- Machine-Learning-Driven Performance Optimization of Nano-Modified Construction Materials: A Bibliometric Analysis and Systematic Review.Nanomaterials (Basel, Switzerland) · 2026Review
- Interpretable machine learning with Bayesian optimization for bond strength prediction of steel reinforcement in geopolymer concrete.PloS one · 2026Article
- Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns: A comparative analysis.PloS one · 2026Article
- Intelligent low carbon reinforced concrete beam design optimization via deep reinforcement learning.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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Concrete compressive strength (CS) is crucial for ensuring the safety, durability, and performance of structures. So, its precise simulation helps anticipate material behavior under various conditions. Despite a comprehensive experimental investigation of the impact of silica (SiO
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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.