为规范公路技术状况检测评价工作,提升公路资产管理和养护决策工作的科学化水平,促进我国公路养护高质量发展,通过文献调研,总结并分析了我国现行路面技术状况评价标准与发达国家在指标选取、分项指标权重设置和路况综合评定等方面的差异;通过实地调研与数据分析相结合的方法,探讨了我国目前路面技术状况检测评价工作中存在的主要问题,特别是对我国现行标准中新增的路面磨耗指标进行了重点分析。研究结果显示:①现阶段我国普遍采用的病害采集技术缺少对变形类病害的识别能力,不完全适应现行标准中综合破损率的要求;②路面磨耗指标计算模型所需参数不明确,磨耗评价结果对实际养护工作的指导性不强;③不同路面类型和技术等级的公路采用相同的等级评价标准,难以客观、科学地反映路面使用性能。研究认为,我国路面技术状况检测评价工作中应推广三维检测技术的应用,优化磨耗指标计算模型,完善等级评价标准。
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.
[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.