Mechanical Properties of Basalt Fiber Reinforced Cemented Silty Sand: Laboratory Tests, Statistical Analysis and Microscopic Mechanism
Benefiting from low cost, high tensile strength, chemical stability, and great resistance to temperature, alkaline, and acids, it is a reasonable and valuable technology to use basalt fiber (BF) as an admixture to optimize building materials. So far, the challenge is still to master the engineering...
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Veröffentlicht in: | Applied sciences 2023-03, Vol.13 (6), p.3493 |
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Sprache: | eng |
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Zusammenfassung: | Benefiting from low cost, high tensile strength, chemical stability, and great resistance to temperature, alkaline, and acids, it is a reasonable and valuable technology to use basalt fiber (BF) as an admixture to optimize building materials. So far, the challenge is still to master the engineering performance of BF-reinforced materials, especially poor subgrade soils. To this end, this paper carried out a series of unconfined compressive strength (UCS) tests, splitting tensile strength (STS) tests, and scanning environmental microscope (SEM) tests to study the mechanical properties and microstructure mechanism of BF-reinforced subgrade cemented silty sand with different fiber contents and curing times. The aims of this research were: (i) the UCS and STS of basalt fiber reinforced uncemented silty sand (BFUSM) and basalt fiber reinforced cemented silty sand (BFCSM) both increased with the increase of curing time and the strength reached the maximum value after curing for 28 days; (ii) the optimal fiber content was 0.2%, and a good linear correlation existed between UCS and STS; (iii) from the microscopic point of view, the combination of BF and cement could combine the physical action of fiber reinforcement and the chemical action of cement hydration reaction to form a fiber-cement-soil skeleton structure to improve the strength of silty sand and the improvement effect after working together was better than separately incorporated BF or cement; and (iv) the corresponding developed multiple nonlinear regression (MNLR) models which can well predict UCS and STS of BFUSM and BFCSM were established. |
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ISSN: | 2076-3417 2076-3417 |
DOI: | 10.3390/app13063493 |