ArticleMaterials (Basel, Switzerland)2022
Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence.
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 14 papers.
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14 citing papers in PubMed.
- A Study on the Influencing Factors of the Mechanical Properties of Steel-Fiber-Reinforced Cement Concrete.Materials (Basel, Switzerland) · 2026Article
- Machine learning-based estimation and optimization of phoenix Dactylifera Seed Powder reinforced vinyl ester bio-composites.Scientific reports · 2026Article
- Influence of Training-Testing Data Variation on ML-Based Deflection Prediction of GFRP-Reinforced High-Strength Concrete Beams.Polymers · 2025Article
- Machine learning-based prediction of crack mouth opening displacement in ultra-high-performance concrete.Scientific reports · 2025Article
- Dynamic Fracture Strength Prediction of HPFRC Using a Feature-Weighted Linear Ensemble Approach.Materials (Basel, Switzerland) · 2025Article
- Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete.Scientific reports · 2025Article
- Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses.Scientific reports · 2025Article
- Efficacy of PZT Sensors Network Different Configurations in Damage Detection of Fiber-Reinforced Concrete Prisms under Repeated Loading.Sensors (Basel, Switzerland) · 2024Article
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- Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques.Materials (Basel, Switzerland) · 2022Article
- Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete.Materials (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R
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