Prediction of Skid Resistance of Steel Slag Asphalt Mixture Based on Grey Residual GM(1,1)-Markov Model Guoxin

To predict the long-term skid resistance of steel slag asphalt mixtures, accelerated wear tests were conducted using an indooraccelerated loading device on the steel slag asphalt mixtures with different aggregate types, different steel slag blends, and different temper-atures. The skid resistance decay law applied to the steel slag asphalt mixture under different influencing factors was investigated. Basedon the skid resistance evaluation index measured during testing, a grey residual grey model (GM)(1,1)-Markov model was established topredict skid resistance. The results showed that the incorporation of steel slag significantly improves skid resistance while helping to reduceskid attenuation loss. Skid resistance increased with the increase in steel slag incorporation. With 100% steel slag incorporation, it wasoptimal. In addition, the test temperature did not change the decay law of the skid resistance index. With changes in temperature, skidresistance showed a decreasing trend. The prediction accuracy of the grey residual GM(1,1)-Markov model was significantly better thanthat of the grey GM(1,1) model and so can be used for skid resistance prediction. The results of the study can help to determine the attenuationcharacteristics of skid resistance of steel slag asphalt mixtures, and provide a simple and reliable method for predicting the skid resistance ofthese mixtures and other pavement aggregates. At the same time, it is certain guidelines for the establishment of the prediction model ofasphalt mixture skid resistance under small sample conditions.