Use of Machine Learning to Predict Long-Term Skid Resistant of Concrete Pavements
| 项目名称： | Use of Machine Learning to Predict Long-Term Skid Resistant of Concrete Pavements |
| 摘要： | This project intends to use laboratory-measured concrete frictional properties data and build a machine learning (ML) model to predict the friction performance of concrete pavements. The model uses aggregate mineralogy, concrete mixture proportions, and concrete surface finish (tined vs. ground and grooved) as input parameters. It predicts the surface friction (measured via the dynamic friction test, DFT, ASTM E 1911; Komaragiri et al., 2020) and surface texture (measured via the circular track meter, CTM, ASTM E 2157; Komaragiri et al., 2020) of concrete pavements undergoing accelerated polishing tests as a function of number of polishing cycles as well as the terminal friction and texture values associated with long-term polishing. The model further uses these outputs to predict the international friction index (IFI) parameters, including the friction number (F60) and slip speed (SP), as well as the equivalent skid number (SN) measured by locked-wheel trailer test (ASTM E524) at a given speed and for a given concrete pavement. |
| 状态： | Active |
| 资金： | $48,546 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 项目负责人： | Donnell, Eric T；Rajabipour, Farshad |
| 执行机构： | Pennsylvania State University, University Park |
| 开始时间： | 20200511 |
| 预计完成日期： | 20210211 |
| 主题领域： | Materials |
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