Performance assessment of sustainable asphalt concrete using steel slag, with an artificial neural network prediction of asphalt concrete behavior

Abstract

This research evaluated the possibility of using Moroccan steelworks slag as a substitute for natural aggregates in asphalt mixtures, using chemical and mechanical analyses to demonstrate its compatibility with this application. Two granular mixtures, M1 and M2, were developed, incorporating respectively 15 % and 25 % natural sand (0/1.25) with slag aggregates to obtain granular mixtures complying with local standards. These mixtures were then tested with three different asphalt contents (4.7 %, 5.2 %, and 5.7 %). Mechanical tests, including a gyratory compactor, Marshall stability, and Duriez strength, were carried out. In particular, the M1:5.2 and M2:5.2 mixtures met compaction criteria and showed remarkable Marshall Stability values, reaching 23.49 kN and 21.81 kN respectively. In comparison, the control mixtures only achieved a maximum stability of 11 kN. Thermal properties were also assessed, showing that the maximum thermal conductivity was achieved at an asphalt content of 5.2 %. The slag-based mixtures, M1 and M2, showed higher thermal conductivity than the control mix M0, with maximum values of 0.74 W/mK for M1 and 0.86 W/mK for M2, compared with 0.56 W/mK for M0. The overall results show that the M1 and M2 mixtures had improved thermal and mechanical properties compared with the M0 control mix. Artificial neural network (ANN) modelling was carried out using Marshall Test data. It showed excellent performance in predicting the stability and flow properties of asphalt mixtures. These results suggest that incorporating steelmaking slag into asphalt concrete mixtures was an effective strategy for simultaneously improving their thermal and mechanical properties. • Moroccan steel slag improves the durability of bituminous mixes in a cost-effective way. • Better thermal conductivity improves road performance in a cost-effective way. • Local Moroccan studies are lacking; this research fills an essential gap. • Artificial neural network effectively optimizes mix design.