[1]丁梦华,蔡宜长,刘晓芳.基于高密度数据和聚类分析的独立车辙识别和评价[J].长安大学学报(自然科学版),2017,37(01):17-23.
　DING Meng-hua,TSAI Yi-chang,LIU Xiao-fang.Isolated rutting identification and evaluation based on high-density data and clustering analysis[J].Journal of Chang’an University (Natural Science Edition),2017,37(01):17-23.
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基于高密度数据和聚类分析的独立车辙识别和评价()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第37卷
期数:
2017年01期
页码:
17-23
栏目:
道路工程
出版日期:
2017-01-31

文章信息/Info

Title:
Isolated rutting identification and evaluation based on high-density data and clustering analysis
作者:
丁梦华; 蔡宜长; 刘晓芳
长安大学 公路学院，陕西 西安 710064
Author(s):
DING Meng-hua;  TSAI Yi-chang;  LIU Xiao-fang
School of Highway, Chang’an University, Xi’an 710064, Shaanxi, China
关键词:
道路工程; 车辙评价; 聚类分析; 独立车辙; 高密度数据
Keywords:
road engineering;  rutting evaluation;  clustering analysis;  isolated rutting;  high-density data
分类号:
U418.6
文献标志码:
A
Abstract:
The average detected rutting value in a certain length was regarded as the rutting evaluation value according to the current specification of “Highway Performance Assessment Standards” (JTG H20—2007). However, average data can smooth the actual rutting depth. To quantify the error in current rutting evaluation, mine the high-density data sufficiently, evaluate rutting more accurately, isolated rutting was defined in this paper, and a rutting identification and evaluation method was proposed based on high-density data and clustering analysis. Isolated rutting identification results obtained using rutting measurement data with different density in driving direction were studied. Case studies, using 13-point laser bar data collected on 1 km and 20 km roadways, respectively, demonstrated the effectiveness and accuracy of the proposed method for identifying and evaluating the severity level and location of isolated rutting. The results were compared with those obtained using the current 1 km average method and the error of these two methods for evaluating rutting were quantified. The results show that the proposed method can be used for all equally-spaced and high-density data, but the current specification using the average rutting depth of 1 km is not able to identify the rutting severity level and location accurately. In a 1 km section, using the proposed method, three isolated ruts are identified, the locations and the severity levels are identified correctly, and the evaluation results are more accurate compared with those using the current method. In a 20 km section, using the current method, 25.1% rutting are identified, among which 18.52% can be accurately evaluated its severity level. However, using the proposed method, all rutting are identified and the accuracy of severity level identification reaches to 82.3%. Current method using the average rutting depth of 1 km is not suitable for evaluating non-uniform rutting. The higher the rutting severity level, the more non-uniform of rutting distribution is, and the higher the rutting identification error is.

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更新日期/Last Update: 2017-01-21