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

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. F...

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Main Authors: Yanhai Yang, Baitong Qian, Qicheng Xu, Ye Yang
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Advances in Civil Engineering
Online Access:http://dx.doi.org/10.1155/2020/6917243
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author Yanhai Yang
Baitong Qian
Qicheng Xu
Ye Yang
author_facet Yanhai Yang
Baitong Qian
Qicheng Xu
Ye Yang
author_sort Yanhai Yang
collection DOAJ
description 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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institution Kabale University
issn 1687-8086
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language English
publishDate 2020-01-01
publisher Wiley
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series Advances in Civil Engineering
spelling doaj-art-c845841b5ed1468f822191495bbcbf552025-02-03T06:44:00ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/69172436917243Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering AlgorithmYanhai Yang0Baitong Qian1Qicheng Xu2Ye Yang3School of Transportation Engineering, Shenyang Jianzhu University, Shenyang 110168, ChinaSchool of Transportation Engineering, Shenyang Jianzhu University, Shenyang 110168, ChinaCollege of Science, Shenyang Jianzhu University, Shenyang 110168, ChinaSchool of Transportation Engineering, Shenyang Jianzhu University, Shenyang 110168, ChinaThe 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.http://dx.doi.org/10.1155/2020/6917243
spellingShingle Yanhai Yang
Baitong Qian
Qicheng Xu
Ye Yang
Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm
Advances in Civil Engineering
title Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm
title_full Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm
title_fullStr Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm
title_full_unstemmed Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm
title_short Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm
title_sort climate regionalization of asphalt pavement based on the k means clustering algorithm
url http://dx.doi.org/10.1155/2020/6917243
work_keys_str_mv AT yanhaiyang climateregionalizationofasphaltpavementbasedonthekmeansclusteringalgorithm
AT baitongqian climateregionalizationofasphaltpavementbasedonthekmeansclusteringalgorithm
AT qichengxu climateregionalizationofasphaltpavementbasedonthekmeansclusteringalgorithm
AT yeyang climateregionalizationofasphaltpavementbasedonthekmeansclusteringalgorithm