Experimental study on the effect of chopped basalt fiber on the mechanical properties of high-performance concrete

Харун, Махмуд; Araza, Hayder A. A. Al; Hematibahar, Mohammad; Daini, R. Al; Manoshin, A. A.

doi:10.1063/5.0099042

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…These conditions show that 1.2% of BF had the greatest effect on the mechanical properties of high-performance concrete. However, the increase in flexural strength was greater than the compressive strength with additional BF, and the main reason for the improvement in flexural strength was the effect of micro-scale bridging phenomena in the cement matrix [ 4 ]. The efficiency of flexural strength ( Figure 15 ) shows that the curing period was effective in improving the strength.…”

Section: Resultsmentioning

confidence: 99%

“…In order to prepare the BFHPC samples, the following material was used: Portland cement (OPC), quartz sand, crushed granite, quartz flour, micro silica, superplasticizer, tap water, and BF [ 4 , 17 ]. The specific gravity of Cement M500 was 1756 Kg/m 3 .…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, the Spyder open source software environment (lessened by Massachusetts Institute of Technology) was used for simpler Python implementation. The mechanical properties of the Basalt-Fibred The High-Performance Concrete were derived from previous experiment results [ 4 , 17 ]. In the current study, BF was the only variable percentage that was able to improve the flexural and compressive strengths.…”

Section: Methodsmentioning

confidence: 99%

“…High-performance concrete (HPC) is a type of concrete with minimal penetration of aggressive agents, and it has become very popular in recent years because it surpasses conventional concrete in terms of its mechanical and chemical properties [ 1 , 2 , 3 ]. The effect of basalt fiber (BF) on HPC was investigated in many previous types of research [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. In fact, BF was added to HPC to increase the durability, tensile, and flexural strengths [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm

Hematibahar

Vatin²,

Alaraza³

et al. 2022

Materials

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In this research, the authors have developed an algorithm for predicting the compressive strength and compressive stress–strain curve of Basalt Fiber High-Performance Concrete (BFHPC), which is enhanced by a classical programming algorithm and Logistic Map. For this purpose, different percentages of basalt fiber from 0.6 to 1.8 are mixed with High-Performance Concrete with high-volume contact of cement, fine and coarse aggregate. Compressive strengths and compressive stress–strain curves are applied after 7-, 14-, and 28-day curing periods. To find the compressive strength and predict the compressive stress–strain curve, the Logistic Map algorithm was prepared through classical programming. The results of this study prove that the logistic map is able to predict the compressive strength and compressive stress–strain of BFHPC with high accuracy. In addition, various types of methods, such as Coefficient of Determination (R2), are applied to ensure the accuracy of the algorithm. For this purpose, the value of R2 was equal to 0.96, which showed that the algorithm is reliable for predicting compressive strength. Finally, it was concluded that The Logistic Map algorithm developed through classical programming could be used as an easy and reliable method to predict the compressive strength and compressive stress–strain of BFHPC.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm

Hematibahar

Vatin²,

Alaraza³

et al. 2022

Materials

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“…According to their results, adding BF may be an effective way for enhancing the mechanical characteristics of HPC. Kharun et al [ 19 ] investigated the effect of adding chopped BF on HPC characteristics. Their results proved that adding 1.2% BF had maximum effects on the improvement of mechanical characteristics.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

et al. 2022

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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 (R2), mean absolute errors (MAE), and root mean square errors (RMSE). According to the obtained values of R2, MAE, and RMSE, the performance of PR was better than other types of algorithms in estimating the compressive, tensile, and flexural strengths. For example, R2 values were 0.99, 0.94, and 0.98 in predicting the compressive, flexural, and tensile strengths using PR, respectively. This shows the higher accuracy and reliability of the PR technique compared with other predictive algorithms. Finally, we concluded that ML techniques can be appropriately applied to assess the mechanical characteristics of BFHPC.

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Evaluation of the mechanical behavior of high-performance concrete (HPC) reinforced with 3D-Printed trusses

Chiadighikaobi,

Hasanzadeh,

Hematibahar

et al. 2024

Results in Engineering

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Experimental study on the effect of chopped basalt fiber on the mechanical properties of high-performance concrete

Cited by 7 publications

References 13 publications

The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm

The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

Evaluation of the mechanical behavior of high-performance concrete (HPC) reinforced with 3D-Printed trusses

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