ArticleMaterials (Basel, Switzerland)2022
Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using 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 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development of eco-friendly HPC using advanced hybrid multi-scale basalt fibers and ground mining materials.Scientific reports · 2026Article
- Mechanical and durability performance prediction of geopolymer incorporating ferrosilicon slag and aluminum powder using machine learning techniques.Scientific reports · 2026Article
- Machine learning models for mechanical properties prediction of basalt fiber-reinforced concrete incorporating graphical user interface.Scientific reports · 2025Article
- Predicting the strengths of basalt fiber reinforced concrete mixed with fly ash using AML and Hoffman and Gardener techniques.Scientific reports · 2025Article
- Modeling the compressive strength behavior of concrete reinforced with basalt fiber.Scientific reports · 2025Article
- The Influence of Three-Dimensionally Printed Polymer Materials as Trusses and Shell Structures on the Mechanical Properties and Load-Bearing Capacity of Reinforced Concrete.Materials (Basel, Switzerland) · 2024Article
- Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach.Materials (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
In this research, we present an efficient implementation of machine learning (ML) models that forecast the mechanical properties of basalt fiber-reinforced high-performance concrete (BFHPC). The objective of the present study was to predict compressive, flexural, and tensile strengths of BFHPC through ML techniques and propose some correlations between these properties. Moreover, the modulus of elasticity (ME) values and compressive stress-strain curves were simulated using ML techniques. In this regard, three predictive algorithms, including linear regression (LR), support vector regression (SVR), and polynomial regression (PR), were considered. LR, SVR, and PR were utilized to forecast the compressive, flexural, and tensile strengths of BFHPC, and the PR technique was employed to simulate the compressive stress-strain curves. The performance of the models was also determined by the coefficient of determination (R
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