ArticleScientific reports2023
Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete.
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 13 papers.
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13 citing papers in PubMed.
- Compressive strength prediction of coconut fiber reinforced concrete using PSO optimized explainable machine learning.Scientific reports · 2026Article
- Machine learning-based estimation and optimization of phoenix Dactylifera Seed Powder reinforced vinyl ester bio-composites.Scientific reports · 2026Article
- Computational Strategy for Analyzing Effective Properties of Random Composites-Part III: Machine Learning.Materials (Basel, Switzerland) · 2025Article
- Early damage detection in bridges using a variational autoencoder-based hybrid unsupervised learning framework.Scientific reports · 2025Article
- Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete.Scientific reports · 2025Article
- Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems.Scientific reports · 2024Article
- Optimizing compressive strength prediction using adversarial learning and hybrid regularization.Scientific reports · 2024Article
- A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering.Scientific reports · 2024Article
- Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP.Scientific reports · 2024Article
- Article
- Effects of the addition of short straight steel fibers on the strength and strains of high-strength concrete during compression.Scientific reports · 2024Article
- Robust Machine Learning Framework for Modeling the Compressive Strength of SFRC: Database Compilation, Predictive Analysis, and Empirical Verification.Materials (Basel, Switzerland) · 2023Article
- Estimation of concrete materials uniaxial compressive strength using soft computing techniques.Heliyon · 2023Article
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3 authors.
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Abstract
Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. However, the understanding of ISF's influence on the compressive strength (CS) behavior of concrete is still questioned by the scientific society. The presented paper aims to use machine learning (ML) and deep learning (DL) algorithms to predict the CS of steel fiber reinforced concrete (SFRC) incorporating hooked ISF based on the data collected from the open literature. Accordingly, 176 sets of data are collected from different journals and conference papers. Based upon the initial sensitivity analysis, the most influential parameters like water-to-cement (W/C) ratio and content of fine aggregates (FA) tend to decrease the CS of SFRC. Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). The least contributing factors include the maximum size of aggregates (D
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