In order to standardize the detection and evaluation of highway conditions, improve the scientific level of highway asset management and maintenance decision-making, and promote the high-quality development of highway maintenance in China, the paper summarized and analyzed the differences of current road condition evaluation standards between China and developed countries in indicator selection, sub-indicator weight setting and comprehensive road condition evaluation; combining field investigation and data analysis, the main problems existing in the current road condition detection and evaluation in China were discussed, especially towards the newly proposed pavement wearing index. This research shows that: ①the distress collection technology commonly used in China at this stage lacks the ability to recognize deformation-type distress and does not fully meet the requirements of the comprehensive distress ratio in the current standards; ②the parameters required for the pavement wearing index calculation model are not clear, and the guidance of wearing evaluation results on actual maintenance is insufficient; ③it is difficult to objectively and scientifically reflect the performance of the pavement using the same grade evaluation standard for different road types and technical grades. The paper argues that the application of three-dimensional detection technology should be further strengthened in the detection and evaluation of road conditions, the calculation model of wearing index should be optimized, and the grade evaluation standards should be improved.
贾国杰，杨碧宇，王俊喆，罗代松
. Comparative Study on Evaluation Index System of Highway Pavement Condition Between China and Developed Countries[J]. Transport Research, 2021
, 7(4)
: 105
-113
.
DOI: 10.16503/j.cnki.2095-9931.2021.04.013
[1] AYDN M M, YILDIRIM M S, FORSLOF L, et al. The use of smart phones to estimate road roughness: a case study in Turkey[J]. New World Sciences Academy, 2018, 13(3): 247-257.
[2] ERSOZ A B, PEKCAN O, TEKE T. Crack identification for rigid pavements using unmanned aerial vehicles[J]. Materials Science and Engineering, 2017, 236: 1-7.
[3] COENEN T, GOLROO A. A review on automated pavement distress detection methods[J]. Cogent Engineering, 2017, 4 (1):1-23.
[4] SALARI E, YU X. Pavement distress detection and classification using a genetic algorithm[C]// Proceedings of 2011 IEEE Applied Imagery Pattern Recognition Workshop. Washington DC: IEEE, 2011, 1: 1-5.
[5] ZAKERI H, NEJAD F M, FAHIMIFAR A. Image based techniques for crack detection, classification and quantification in asphalt pavement: A review[J]. Archives of Computational Methods in Engineering, 2017, 24(4): 935-977.
[6] ZHAO H L, QIN G F, WANG X J. Improvement of canny algorithm based on pavement edge detection[C]// Proceedings of 2010 3rd International Congress on Image and Signal Processing. Yantai, China: IEEE, 2010: 964-967.
[7] CHENG H D, WANG J, HU Y G, et al. Novel approach to pavement cracking detection based on neural network[J]. Transportation Research Record: Journal of the Transportation Research Board, 2001, 1764:119-127.
[8] AVILA M, BEGOT S, DUCULTY F, et al. 2D image based road pavement crack detection by calculating minimal paths and dynamic programming[C]// Proceedings of 2014 IEEE International Conference on Image Processing. Paris: IEEE, 2014: 783-787.
[9] 张艳红，申爱琴，侯芸. 资金-目标双优化法在路面养护决策中的应用[J]. 公路交通科技， 2018，35（9）：34-40.
[10] 郑育彬，柏强，陈琳，等. 美国宾夕法尼亚州绩效式路面养护维修需求案例分析[J]. 东南大学学报（英文版），2019，35（2）：242-251.
[11] National Academy of Sciences-National Research Council. The AASHO road test history and description of project[R]. Washington DC: National Academy of Sciences-National Research Council, 1961.
[12] Highways Agency. Pavement design and maintenance: Section 3 Pavement maintenance assessment Part 2 Data for pavement assessment: HD 29/08[S]. Norwich, UK: The Stationery Office, 2008.
[13] 潘玉利. 路面管理系统原理[M]. 北京：人民交通出版社，1998.
[14] HAAS R, HUDSON W R, FALLS L C. Pavement asset management[M]. Beverly, US: Scrivener Publishing LLC, 2015.
[15] PIERCE L M, MCGOVERN G, ZIMMERMAN K A. Practical guide for quality management of pavement condition data collection[R]. Washington DC: FHWA, 2013.
[16] Subcommittee E17.42 on pavement management and data needs. Standard practice for roads and parking lots pavement condition index surveys: ASTM D6433-09[S]. West Conshohocken, US: ASTM International, 2009.
[17] 交通运输部公路科学研究院. 公路技术状况评定标准：JTG 5210—2018[S]. 北京：人民交通出版社股份有限公司，2018.
[18] LI N Y, KAZMIEROWSKI T, KOO A. Key pavement performance indicators and prediction models applied in a Canadian PMS[R]. Santiago, Chile: 8th International Conference on Managing Pavement Assets, 2011.
[19] New York State Department of Transportation. Network level condition assessment procedures[R]. New York: New York State Department of Transportation, 2010.
[20] 交通运输部公路科学研究院. 公路路面技术状况自动化检测规程：JTG/T E61—2014[S]. 北京： 人民交通出版社，2014.
[21] 窦光武. 基于断面高程的路面构造深度计算模型研究[J]. 公路交通科技，2015，32（1）：50-56.
[22] 全国交通工程设施（公路）标准化技术委员会. 多功能路况快速检测设备：GB/T 26764—2011[S]. 北京：中国标准出版社，2011.
[23] CHAMORRO A , TIGHE S L , LI N Y, et al. Development of distress guidelines and condition rating to improve network management in Ontario, Canada[J]. Journal of the Transportation Research Board, 2009, 2093(1): 128-135.
