Siyu Chen , Xiyin Liu , Haoyuan Luo , Jiangmiao Yu , Fuda Chen , Yang Zhang , Tao Ma , Xiaoming Huang
{"title":"A state-of-the-art review of asphalt pavement surface texture and its measurement techniques","authors":"Siyu Chen , Xiyin Liu , Haoyuan Luo , Jiangmiao Yu , Fuda Chen , Yang Zhang , Tao Ma , Xiaoming Huang","doi":"10.1016/j.jreng.2022.05.003","DOIUrl":null,"url":null,"abstract":"<div><p>To understand the research status of asphalt pavement texture, the related achievements and progress of pavement surface texture were systematically sorted out from three aspects: the characterization of pavement surface texture, the texture measurement and evaluation, and the relationship between texture and the skid resistance. Based on the statistical geometric characteristics, the spectral characteristics, the fractal characteristics and the multifractal characteristics, the characteristics of pavement texture were discussed. The test methods of pavement texture were divided into two categories: direct measurement methods and indirect measurement methods. The advantages and disadvantages of each measurement method were summarized. The effects of macro-texture and micro-texture on asphalt pavement were discussed, respectively. The relationship between texture and skid resistance was studied. This review shows that multifractal theory should be further studied from the aspect of road engineering. High-precision non-contact integrated detection technology should be further studied to meet the needs of complex testing environments. The method of finite element numerical simulation has potential for the analysis of pavement skid resistance. In addition, methods such as big data analysis, neural network, and deep learning should be studied to achieve the intelligent perception and management of the whole state of skid resistance prediction.</p></div>","PeriodicalId":100830,"journal":{"name":"Journal of Road Engineering","volume":"2 2","pages":"Pages 156-180"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2097049822000208/pdfft?md5=0ee48b2750fb75b7a33fd98b001441ac&pid=1-s2.0-S2097049822000208-main.pdf","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Road Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2097049822000208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
To understand the research status of asphalt pavement texture, the related achievements and progress of pavement surface texture were systematically sorted out from three aspects: the characterization of pavement surface texture, the texture measurement and evaluation, and the relationship between texture and the skid resistance. Based on the statistical geometric characteristics, the spectral characteristics, the fractal characteristics and the multifractal characteristics, the characteristics of pavement texture were discussed. The test methods of pavement texture were divided into two categories: direct measurement methods and indirect measurement methods. The advantages and disadvantages of each measurement method were summarized. The effects of macro-texture and micro-texture on asphalt pavement were discussed, respectively. The relationship between texture and skid resistance was studied. This review shows that multifractal theory should be further studied from the aspect of road engineering. High-precision non-contact integrated detection technology should be further studied to meet the needs of complex testing environments. The method of finite element numerical simulation has potential for the analysis of pavement skid resistance. In addition, methods such as big data analysis, neural network, and deep learning should be studied to achieve the intelligent perception and management of the whole state of skid resistance prediction.