Climate Regionalization of Asphalt Pavement Based on the K‐Means Clustering Algorithm

Abstract

The climate regionalization of asphalt pavement plays an active role in ensuring the good performance and service life of asphalt pavement. In order to better adapt to the climate characteristics of a region, this study developed a multi-index method of climate regionalization of asphalt pavement. First, meteorological data from the research region were statistically analyzed and the major climate variables were identified. Then, a principal component analysis (PCA) was used to eliminate any correlation between the major climate variables. Three principal components were extracted by the PCA as cluster factors, namely, the temperature factor, precipitation factor, and radiation factor. The research region was divided into the following four asphalt pavement climate zones via the K-means clustering algorithm. Those zones are affected by the climate comprehensively: an inland zone with high temperatures, little rainfall, and radiation, a coastal zone with high temperatures, and a rainy mountainous zone. The results of the climate regionalization were compared with the results of on-site investigations. The pavement degradation in each climatic zone was related to the climate characteristics of the region. Probabilistic neural network (PNN) and support vector machine (SVM) climate regionalization predictive models were established with MATLAB. The clustering factors were used as the input data to identify the climate zones, and the identification accuracy rate was determined to be over 90%. The climate regionalization of pavement can provide reference and guidance for the selection of reasonable technical measures, parameters, and building materials in highway projects with similar climatic conditions.

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