ArticleMaterials (Basel, Switzerland)2022
Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
2 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
- Innovative approaches for predicting the elastic modulus and tensile strength of green concrete using soft computing methods.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
This research examined machine learning (ML) techniques for predicting the compressive strength (CS) of self-compacting concrete (SCC). Multilayer perceptron (MLP), bagging regressor (BR), and support vector machine (SVM) were utilized for analysis. A total of 169 data points were retrieved from the various published articles. The data set was based on 11 input parameters, such as cement, limestone, fly ash, ground granulated blast-furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, superplasticizers, water, viscosity modifying admixtures, and one output with compressive strength of SCC. In terms of properly predicting the CS of SCC, the BR technique outperformed both the SVM and MLP models, as determined by the research results. In contrast to SVM and MLP, the coefficient of determination (R
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