PERFORMANCE EVALUATION OF SLAG MODIFIED CONCRETE USING MACHINE LEARNING TECHNIQUES

Akintayo Adeniji; Williams  Kupolati; Everardt A. Burger; Tolulope Olatunji; Jacques Snyman

doi:10.30572/2018/kje/170302

Authors

Akintayo Adeniji Department of civil engineering,tshwane university of technology, pretoria, south Africa
Williams Kupolati Department of civil engineering,tshwane university of technology, pretoria, south Africa
Everardt A. Burger Department of civil engineering,tshwane university of technology, pretoria, south Africa
Tolulope Olatunji Department of civil engineering, university of ibadan, ibadan, Nigeria
Jacques Snyman Department of civil engineering,tshwane university of technology, pretoria, south Africa

DOI:

https://doi.org/10.30572/2018/kje/170302

Keywords:

Concrete, steel slag, compressive strength, flexural strength, machine learning

Abstract

Global demand for sustainable construction materials and concerns about environmental pollution from by-products of manufacturing industries have intensified research for viable alternatives to aggregates in concrete production. This research investigated steel slag aggregate (ssa) as a partial replacement for coarse aggregates. Samples of 150 mm concrete cubes and 100 x 100 x 500 mm prisms were prepared at a 1:2:4 mix ratio and a water-cement ratio of 0.5, with ssa replacing coarse aggregates at 0%, 15%, 30%, 45%, and 60% by weight for 7, 14, and 28 days compressive and flexural strength tests. In addition, a validated random forest (rf) and multiple linear regression (mlr) algorithm were developed to predict the compressive strength of samples. The analysis of ssa for x-ray fluorescence (xrf) revealed a high 34.125% silicon dioxide (sio₂) composition and a minimum of 0.196% for strontium oxide (sro), a specific gravity of 3.07, 1680 kg/m³ bulk density, 21.28% and 8.37% aggregate crushing value and impact value were evaluated, respectively. The 15%, 30%, 45%, and 60% ssa replacement samples exhibited improved strengths of 15.63 n/mm², 17.07 n/mm², 18.30 n/mm², and 19.10 n/mm², while the flexural strengths increased up to 45% ssa (4.45 n/mm²) before declining at 60% (3.85 n/mm²). The mean absolute error (mae), mean square error (mse) and a coefficient of determination (r²) for rf were 1.20, 2.07 and 0.85, while mlr recorded 1.46, 3.77 and 0.72, respectively. The xrf suggests an improved aggregate bonding potential, and the physical characterisation revealed that ssa was within the acceptable limits for structural applications. The compressive and flexural strengths increased with ssa content up to 45%, after which strength properties declined, offering optimal mechanical performance. The mlr achieved a robust prediction accuracy with an r² value of 0.85. In conclusion, the research supports the potential of industrial by-products in promoting greener and more cost-effective construction practices

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