ArticleScientific reports2023
Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques.
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 3 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
3 citing papers in PubMed.
- Identification of Selected Physical and Mechanical Properties of Cement Composites Modified with Granite Powder Using Neural Networks.Materials (Basel, Switzerland) · 2025Article
- Interpretable machine learning approaches to assess the compressive strength of metakaolin blended sustainable cement mortar.Scientific reports · 2025Article
- A hybrid artificial intelligence approach for modeling the carbonation depth of sustainable concrete containing fly ash.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Splitting tensile strength (STS) is an important mechanical property of concrete. Modeling and predicting the STS of concrete containing Metakaolin is an important method for analyzing the mechanical properties. In this paper, four machine learning models, namely, Artificial Neural Network (ANN), support vector regression (SVR), random forest (RF), and Gradient Boosting Decision Tree (GBDT) were employed to predict the STS. The comprehensive comparison of predictive performance was conducted using evaluation metrics. The results indicate that, compared to other models, the GBDT model exhibits the best test performance with an R
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Registered trials
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