Correlation of Phase Composition, Structure, and Mechanical Properties of Natural Basalt Continuous Fibers

Gutnikov, Sergey I.; Попов, С. С.; Ефремов, В. Н.; Ma, Peng‐Cheng; Lazoryak, Bogdan I.

doi:10.1007/s11053-020-09786-1

Cited by 17 publications

(2 citation statements)

References 41 publications

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“…Literature research shows that the chemical composition of basalt fiber, calculated by oxide content, has a significant impact on tensile strength 17,22,23 . Furthermore, acidity modulus (Ma), viscosity modulus (Mv), and the degree of depolymerization of the melt, expressed by NBO/T, which is calculated by the ratio of the molar content of non‐bridging oxygen (NBO) to the tetrahedrally coordinated cation (T) are closely associated with the basalt melt and mechanical properties of BCF 42–46 . Therefore, oxides and their derived parameters of Ma, Mv, and NBO/T are chosen as the feature variables to predict the target variable of tensile strength.…”

Section: Experimental Methodsmentioning

confidence: 99%

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

et al. 2023

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Tensile strength is the major mechanical property of basalt continuous fiber and is closely related to their chemical composition. This study constructed a machine learning model framework to predict the tensile strength of basalt continuous fiber. The database included the characteristic variables of oxides and their derived parameters, as well as the target variables of tensile strength. The mean squared error (MSE) were calculated to evaluate the performance of six machine learning models of decision tree, kernel ridge regression, multivariable linear regression, support vector regression, random forest, and k‐nearest neighbors. The k‐nearest neighbors model had a minimum MSE and was tuned using the grid search method with cross‐validation. The optimal hyper‐parameters of the k‐nearest neighbors model were K = 6, p = 10, and the determination coefficient of the model reached the maximum of 0.7110 on the test data. The limitations and implications of the present study were also demonstrated. We finally expected that the constructed framework could achieve higher prediction accuracy for tensile strength at a low cost in the future.Highlights A machine learning framework was built to predict the tensile strength of BCF. Oxides and their derived parameters were used for modeling. K‐nearest neighbors performed best in predicting the tensile strength. The framework was robust, cost‐saving, and efficient.

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Section: Experimental Methodsmentioning

confidence: 99%

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

et al. 2023

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“…Continuous basalt fiber (CBF) is a new inorganic fiber material [8]. It has the advantages of natural raw materials, high comprehensive performance, low cost, and better compatibility with concrete [9][10][11][12][13][14][15][16][17]. Compared with continuous fibers such as carbon fiber, glass fiber, and aramid fiber, it has better physical and mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

Low-Cycle Reverse Loading Tests of the Continuous Basalt Fiber-Reinforced Polymer Column Filled with Concrete

Liu

Guo

et al. 2022

Applied Sciences

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The continuous basalt fiber-reinforced polymer column filled with concrete (BFRPC composite column) can well resist the erosion of the external environment, improve the durability of the structure, and reduce the life-cycle cost of the project. To evaluate the mechanical behaviors of the BFRPC composite column under cyclic lateral loading, laboratory low-cycle reverse loading tests were implemented on a BFRPC composite column specimen, a prestressed reinforced concrete (PRC) tube column specimen, and a prestressed high-strength concrete (PHC) tube column specimen. The failure features, hysteretic curve, and skeleton curve for these three types of column specimens were compared and analyzed through the load–displacement hysteretic curve. The results indicated that the BFRPC composite column possesses the better bearing capacity and deformation performance. The horizontal bearing capacity of the BFRPC composite column is at least three times better than that of PHC and PRC tube columns. Finally, the functional expression of the skeleton curves for the BFRPC composite column is fitted by the rational function fitting method.

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Influence of Modifiers on the Properties of Basalt Glasses and Melts

Pavlyukevich,

Papko,

Kravchuk

et al. 2023

Glass Ceram

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Correlation of Phase Composition, Structure, and Mechanical Properties of Natural Basalt Continuous Fibers

Cited by 17 publications

References 41 publications

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

Low-Cycle Reverse Loading Tests of the Continuous Basalt Fiber-Reinforced Polymer Column Filled with Concrete

Influence of Modifiers on the Properties of Basalt Glasses and Melts

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