In order to solve the international roughness index problem of asphalt concrete pavement in seasonal frozen area, this article takes four typical highways from China's seasonally frozen regions as examples to organize and analyze the geographic location, climatic conditions, structural layer materials and traffic volume of the four roads. Based on the mechanistic-empirical pavement design guide, and by the statistical product and service solutions software for regression analysis, propose IRI correction prediction model of asphalt concrete pavement in the seasonal frozen area and choose IRI measuring values of other highways and the predicted values of IRI prediction model to verify. The result shows that there is a linear relationship between international roughness index, environmental factor, fatigue crack area, transverse crack length and average rut depth. The coefficient of determination R2 is 0.999, the adjusted R2 is 0.999, the significance level is 0, and the regression model is effective. The values corresponding to modified model, environmental factor, fatigue crack area, transverse crack length and average rut depth indicators are 0.004, 0.074, 0.143 and 51.563 respectively; The IRI predicted value of the modified model is closer to the measured value than that of the traditional prediction model. The research results are of great significance for the international roughness index prediction of an asphalt concrete pavement in a seasonal frozen area.

In order to solve the international roughness index problem of asphalt concrete pavement in seasonal frozen area, this article takes four typical highways from China's seasonally frozen regions as examples to organize and analyze the geographic location, climatic conditions, structural layer materials and traffic volume of the four roads. Based on the mechanistic-empirical pavement design guide, and by the statistical product and service solutions software for regression analysis, propose IRI correction prediction model of asphalt concrete pavement in the seasonal frozen area and choose IRI measuring values of other highways and the predicted values of IRI prediction model to verify. The result shows that there is a linear relationship between international roughness index, environmental factor, fatigue crack area, transverse crack length and average rut depth. The coefficient of determination R2 is 0.999, the adjusted R2 is 0.999, the significance level is 0, and the regression model is effective. The values corresponding to modified model, environmental factor, fatigue crack area, transverse crack length and average rut depth indicators are 0.004, 0.074, 0.143 and 51.563 respectively; The IRI predicted value of the modified model is closer to the measured value than that of the traditional prediction model. The research results are of great significance for the international roughness index prediction of an asphalt concrete pavement in a seasonal frozen area.