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

    Source: Journal of Materials in Civil Engineering:;2024:;Volume ( 036 ):;issue: 001::page 04023518-1
    Author:
    Guoxin Chen
    ,
    Jiaqi Luo
    DOI: 10.1061/JMCEE7.MTENG-16280
    Publisher: ASCE
    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.
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      Prediction of Skid Resistance of Steel Slag Asphalt Mixture Based on Grey Residual GM(1,1)-Markov Model

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    contributor authorGuoxin Chen
    contributor authorJiaqi Luo
    date accessioned2024-04-27T22:55:51Z
    date available2024-04-27T22:55:51Z
    date issued2024/01/01
    identifier other10.1061-JMCEE7.MTENG-16280.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4297858
    description abstractTo 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.
    publisherASCE
    titlePrediction of Skid Resistance of Steel Slag Asphalt Mixture Based on Grey Residual GM(1,1)-Markov Model
    typeJournal Article
    journal volume36
    journal issue1
    journal titleJournal of Materials in Civil Engineering
    identifier doi10.1061/JMCEE7.MTENG-16280
    journal fristpage04023518-1
    journal lastpage04023518-11
    page11
    treeJournal of Materials in Civil Engineering:;2024:;Volume ( 036 ):;issue: 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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