| 路面抗滑性能检测与预估方法综述 |
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| 引用本文： | 谭忆秋,肖神清,熊学堂.路面抗滑性能检测与预估方法综述[J].交通运输工程学报,2021,21(4):32-47. |
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| 作者姓名： | 谭忆秋 肖神清 熊学堂 |
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| 作者单位： | 1.哈尔滨工业大学 交通科学与工程学院，黑龙江 哈尔滨 1500902.哈尔滨工业大学 城市水资源与水环境国家重点实验室，黑龙江 哈尔滨 150090 |
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| 基金项目： | 国家自然科学基金项目U20A20315国家重点研发计划项目2016YFE0202400 |
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| 摘 要： | 针对道路工程中路面抗滑性能检测与预估中存在的问题，分别从力学机理、检测方法、预估模型3个方面系统梳理了路面抗滑性能相关成果及进展；基于传统的库伦摩擦定律，阐明了路面抗滑性能的摩擦力学机理，从路面、轮胎以及接触环境3个方面总结了抗滑性能的影响因素；总结了抗滑性能的直接与间接测量方法，重点分析了路表纹理检测技术的难点以及测试数据的预处理方法；对比分析了抗滑性能预估的传统经验统计模型、力学模型以及机器学习等方法的优点与不足。研究结果表明:影响路面抗滑性能的因素众多，传统的摩擦理论难以描述橡胶与粗糙表面接触界面第三体的力学行为，因此，需要进一步研究具有润滑介质的接触界面摩擦机理；抗滑性能直接检测方法功能单一，成本较高，表面纹理的高速无接触自动化检测更加符合未来智能一体化检测需求，但高精度、大量程检测以及数据清洗仍是需要突破的瓶颈；相比现行的各类预估模型，经验统计模型及机器学习弱化了胎-路接触特性，导致预估模型缺乏扩展性；推行有限元仿真力学模型方法，有望进一步揭示复杂物理场下的摩擦机理，从而开发更精准、高效的路面抗滑预估模型。
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| 关 键 词： | 道路工程 抗滑性能 智能检测 预估方法 机器学习 |
| 收稿时间： | 2021-03-15 |
Review on detection and prediction methods for pavement skid resistance |
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| TAN Yi-qiu,XIAO Shen-qing,XIONG Xue-tang.Review on detection and prediction methods for pavement skid resistance[J].Journal of Traffic and Transportation Engineering,2021,21(4):32-47. |
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| Authors: | TAN Yi-qiu XIAO Shen-qing XIONG Xue-tang |
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| Affiliation: | 1.School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilongjiang, China2.State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, Heilongjiang, China |
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| Abstract: | The relevant achievements and progress of skid resistance performance of pavements were systematically reviewed based on three aspects: mechanical mechanism, detection methods, and prediction models. The friction mechanism of pavement skid resistance was introduced based on the traditional Coulomb friction law, and the factors influencing the skid resistance were summarized based on road surface, tire, and contact environment. The direct and indirect measurement methods of skid resistance were evaluated, and the difficulties of road-surface texture detection and test data preprocessing methods were analyzed. The advantages and disadvantages of the skid resistance prediction methods, including traditional empirical statistical models, mechanical models, and machine learning, were compared and analyzed. Analysis results show that many factors influence the skid resistance of pavements, and it is difficult to describe the mechanical behavior of the third body between rubber and rough surface. Thus, further investigations are required to reveal the friction mechanism toward the contact interface with the lubrication medium.For the single function and high cost of the direct detection of skid resistance, surface texture detection using automatic high-speed noncontact measurements will be more in line with future intelligent integrated requirements. However, high-precision and large-range detection and data cleaning are still bottlenecks that need to break through. Compared to the existing prediction models, the tire-pavement contact characteristics are weakened by the empirical statistical model and machine learning, resulting in a lack of scalability in the prediction model. The implementation of the finite element simulation model method is expected to reveal the friction mechanism under complex physical fields to develop a more precise and efficient model for predicting the skid resistance of pavements. 1 tab, 13 figs, 89 refs. |
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