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

Abstract

To predict the long-term skid resistance of steel slag asphalt mixtures, accelerated wear tests were conducted using an indoor accelerated loading device on the steel slag asphalt mixtures with different aggregate types, different steel slag blends, and different temperatures. The skid resistance decay law applied to the steel slag asphalt mixture under different influencing factors was investigated. Based on the skid resistance evaluation index measured during testing, a grey residual grey model (GM)(1,1)-Markov model was established to predict skid resistance. The results showed that the incorporation of steel slag significantly improves skid resistance while helping to reduce skid attenuation loss. Skid resistance increased with the increase in steel slag incorporation. With 100% steel slag incorporation, it was optimal. In addition, the test temperature did not change the decay law of the skid resistance index. With changes in temperature, skid resistance showed a decreasing trend. The prediction accuracy of the grey residual GM(1,1)-Markov model was significantly better than that 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 attenuation characteristics of skid resistance of steel slag asphalt mixtures, and provide a simple and reliable method for predicting the skid resistance of these mixtures and other pavement aggregates. At the same time, it is certain guidelines for the establishment of the prediction model of asphalt mixture skid resistance under small sample conditions.