Predictive Models for Mechanical Properties of Hybrid Fibres reinforced Concrete Containing Bamboo and Basalt Fibres

Shaaban, Ibrahim ORCID: https://orcid.org/0000-0003-4051-341X, Choo Chin, Siew, Rizzuto, Joseph, Khan, Sadaqat, Mohamed, Diana, Iman Muhammad Roslan, Nural and Abdul Aziz, Abdul (2024) Predictive Models for Mechanical Properties of Hybrid Fibres reinforced Concrete Containing Bamboo and Basalt Fibres. Structures, 61. ISSN 2352-0124

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Abstract

The aim of this study was to investigate hybrid fibre reinforced concrete containing natural Bamboo and basalt fibres and forecasting the effect of these fibres on Mechanical properties. The contents of Basalt fibres were (0%, 0.25%, 0.50%, 0.75% and 1.00%). The mechanical performance of the hybrid fibre-reinforced concrete was studied in terms of compressive, splitting tensile and flexural strengths. All the samples were tested at the ages of 7, 14, and 28 days. The results showed that an increase in fibre percentages led to a reduction in the concrete slump. It was also found that 0.75% Basalt fibres with 1% bamboo fibres resulted in the optimum performance of the mechanical properties of the concrete. Based on regression models, it was found that bamboo fibres have negative impact on the compressive and splitting tensile strength while basalt fibres enhanced this strength, however, the negative effect of bamboo fibres reduces with the age of concrete. While the effect of bamboo fibres on flexural strength was positive and basalt fibres have negative impact on it. It was concluded that the bamboo and basalt fibre have good relation in overall improving the mechanical properties of hybrid fibre reinforced concrete.

Item Type: Article
Identifier: 10.1016/j.istruc.2024.106093
Additional Information: Gold OA - Read and Publish
Keywords: Hybrid fibre-reinforced concrete; natural Bamboo fibres; Basalt fibres; mechanical properties. Regression Models
Subjects: Construction and engineering
Depositing User: Eilish McLaughlin
Date Deposited: 28 Feb 2024 10:45
Last Modified: 04 Nov 2024 11:25
URI: https://repository.uwl.ac.uk/id/eprint/11232

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