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Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm
Joint Authors
Yang, Yanhai
Qian, Baitong
Xu, Qicheng
Yang, Ye
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-22
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yang, Yanhai& Qian, Baitong& Xu, Qicheng& Yang, Ye. 2020. Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1122586
Modern Language Association (MLA)
Yang, Yanhai…[et al.]. Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm. Advances in Civil Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1122586
American Medical Association (AMA)
Yang, Yanhai& Qian, Baitong& Xu, Qicheng& Yang, Ye. Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1122586
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1122586