ArticleMaterials (Basel, Switzerland)2022
Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques.
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 15 papers.
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15 citing papers in PubMed.
- Evaluate the compressive strength of fiber-reinforced geopolymer concrete incorporating ground granulated blast furnace slag with machine learning approach.Scientific reports · 2026Article
- Integration of machine learning and microstructural characterization for strength forecasting with silica fume and M-sand for sustainable concrete.Scientific reports · 2026Article
- Optimizing Early Compressive Strength of Ultrafine POFA-Based Green HSC: An Experimental and RSM Approach with Silica Fume and Hybrid Fibers.F1000Research · 2026Article
- Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete.Scientific reports · 2025Article
- Machine learning approaches for forecasting compressive strength of high-strength concrete.Scientific reports · 2025Article
- Evaluating the strength of industrial wastesbased concrete reinforced with steel fiber using advanced machine learning.Scientific reports · 2025Article
- Editorial for "Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II)".Materials (Basel, Switzerland) · 2025Article
- Article
- Study on the Influence of Steel Fiber Distribution on the Mechanical Properties of Perfobond Leiste (PBL) Shear Connectors.Materials (Basel, Switzerland) · 2023Article
- Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming.Scientific reports · 2023Article
- Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach.Materials (Basel, Switzerland) · 2023Article
- Article
- Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete.Scientific reports · 2023Article
- Prediction of the shear capacity of ultrahigh-performance concrete beams using neural network and genetic algorithm.Scientific reports · 2023Article
- Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence.Materials (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber-reinforced concrete (SFRC), machine learning techniques, i.e., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R
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