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Research Article

RSM based modelling and optimization of Marshall properties of steel-slag and lime-modified asphalt mixtures

Oguntayo Daniel1, Ogundipe Olumide2, Aluko Oluwasegun2, Babatunde Yusuf3

1Dept. of Civil and Mining Engineering, Confluence University of Science and Technology, Osara, Nigeria
2Civil Engineering Department, Ekiti State University, Ado-Ekiti, Nigeria
3Civil Engineering Department, University of Ilorin, Ilorin, Nigeria

Keywords

Abstract


Response surface methodology; 

 Box behnken design; 

Modified asphalt mixtures; 

 Steel slag; 

 Lime; 

 Marginal aggregates



Statistical and optimization techniques are useful tools in understanding the interactions and relationships between the asphalt mix variables and the contribution of each variable to the resulting asphalt mixture properties. This study explores the response surface methodology to study the influence of steel slag and lime on the Marshall properties of asphalt mixtures using Box Behnken Design tool. The independent variables considered are steel slag (0-100%), lime (0-4%) and bitumen (4-8%). The stability, flow, Marshall quotients, bulk density, Vb and VMA obtained were 1.98-6.35 kN, 3.27-4.53 mm, 0.53-1.60 kN/mm, 2.08-2.29 kg/m3, 7.67-14.7% and 14.02-24.63%, respectively; indicating that the steel-slag and lime-modified asphalt mixtures satisfy the specification limits recommended by the asphalt institute and Nigeria General Specification for Roads and Bridges. The models' analysis of variance revealed could well predict the Marshall properties of the mixtures, and the terms of steel-slag, lime and bitumen content are significant. Likewise, based on the optimization analysis, 24.93% of steel slag, 2.43% of lime and 5.51% of bitumen content were selected as the optimal values for the modified asphalt mixtures. Additionally, a mean error of less than 5% was attained for all the responses, demonstrating the effectiveness of RSM in designing asphalt mixtures.

© 2024 MIM Research Group. All rights reserved.

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2023 Reviewer Awards:

Please, visit Reviewer Awards section for the winners of the 2022 RESM reviewer awards.



2023 Best Paper Award:

The paper authored by Ferzan Fidan, Naim Aslan, Mümin Mehmet Koç entitled as “Morpho-structural and compressive mechanical properties of graphene oxide reinforced hydroxyapatite scaffolds for bone tissue applications” is awarded.



2023 Most Cited Paper Award:

The paper authored by Ercan Işık, Ehsan Harirchian, Hüseyin Bilgin, Kirti Jadhav entitled as “The effect of material strength and discontinuity in RC structures according to different site-specific design spectra" is awarded.


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