Empirical models for compressive and tensile strength of basalt fiber reinforced concrete

Muhammad Asghar; Muhammad Faisal Javed; M Ijaz Khan; Sherzod Abdullaev; Fuad A Awwad; Emad A A Ismail

doi:10.1038/s41598-023-47330-2

Empirical models for compressive and tensile strength of basalt fiber reinforced concrete

Sci Rep. 2023 Nov 14;13(1):19909. doi: 10.1038/s41598-023-47330-2.

Authors

Muhammad Asghar¹, Muhammad Faisal Javed², M Ijaz Khan^{3

4

5}, Sherzod Abdullaev^{6

7}, Fuad A Awwad⁸, Emad A A Ismail⁸

Affiliations

¹ Department of Geotechnical Engineering, NICE, National University of Science and Technology, Islamabad, Pakistan.
² Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Islamabad, Pakistan.
³ Department of Mechanical Engineering, Lebanese American University, Beirut, Lebanon. scientificresearchglobe@gmail.com.
⁴ Department of Mathematics and Statistics, Riphah International University I-14, Islamabad, 44000, Pakistan. scientificresearchglobe@gmail.com.
⁵ Department of Mechanics and Engineering Science, Peking University, Beijing 100871, China. scientificresearchglobe@gmail.com.
⁶ Faculty of Chemical Engineering, New Uzbekistan University, Tashkent, Uzbekistan.
⁷ Department of Science and Innovation, Tashkent State Pedagogical University Named After Nizami, Bunyodkor Street 27, Tashkent, Uzbekistan.
⁸ Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, 11587, Riyadh, Saudi Arabia.

Abstract

When molten magma solidifies, basalt fiber (BF) is produced as a byproduct. Due to its remaining pollutants that could affect the environment, it is regarded as a waste product. To determine the compressive strength (CS) and tensile strength (TS) of basalt fiber reinforced concrete (BFRC), this study will develop empirical models using gene expression programming (GEP), Artificial Neural Network (ANN) and Extreme Gradient Boosting (XG Boost). A thorough search of the literature was done to compile a variety of information on the CS and TS of BFRC. 153 CS findings and 127 TS outcomes were included in the review. The water-to-cement, BF, fiber length (FL), and coarse aggregates ratios were the influential characteristics found. The outcomes showed that GEP can accurately forecast the CS and TS of BFRC as compared to ANN and XG Boost. Efficiency of GEP was validated by comparing Regression (R²) value of all three models. It was shown that the CS and TS of BFRC increased initially up to a certain limit and then started decreasing as the BF % and FL increased. The ideal BF content for industrial-scale BF reinforcement of concrete was investigated in this study which could be an economical solution for production of BFRC on industrial scale.