Isolated rutting identification and evaluation based on high-density data and clustering analysis(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2017年01期
- Page:
- 17-23
- Research Field:
- 道路工程
- Publishing date:
Info
- Title:
- Isolated rutting identification and evaluation based on high-density data and clustering analysis
- Author(s):
- DING Meng-hua; TSAI Yi-chang; LIU Xiao-fang
- School of Highway, Chang’an University, Xi’an 710064, Shaanxi, China
- Keywords:
- road engineering; rutting evaluation; clustering analysis; isolated rutting; high-density data
- PACS:
- U418.6
- DOI:
- -
- 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.
Last Update: 2017-01-21