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PURPOSES : The type and degree of structural conditions and influencing factors distributed across representative sections should be similar to those distributed across entire sections as the representative sections have been predominantly used for developing performance prediction models, which substitute entire sections of road pavement. Therefore, a logic that selects the representative sections with similar distributions of structural conditions and the influencing factors with those of entire expressway asphalt pavement sections requires development. METHODS : The logic developed in this study to select the representative sections of asphalt pavements comprised three steps. First, the data on the structural conditions of the pavement and the influencing climate conditions and pavement materials were collected and organized. Consequently, in the second step, the candidate sections were selected, with the severity of the structural conditions of the pavement distributed widely and evenly. Finally, in addition to the widely and evenly distributed pavement conditions, the representative sections with climatic conditions and pavement materials were selected. RESULTS : A total of 6,352 ordinary asphalt pavement sections and 596 composite asphalt pavement sections were selected as entire expressway asphalt pavement sections and the data were collected and organized according to the logic developed in this study. Three times the representation sections were selected as candidate sections and, finally, 85 sections were selected as representative sections. The distribution of structural conditions and influencing climate conditions and pavement materials in the representative sections were similar to those in the entire sections. In addition, the representative sections were spread evenly across the country. CONCLUSIONS : The sections presenting similar distributions of structural conditions and the influencing factors of entire expressway asphalt pavement sections could be selected in this study. Using the representative sections selected in this study, a remodeling index model will be developed for predicting the asphalt pavement sections that require large-scale repair.
