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Validation Study of Road Surface Water Film Depth Prediction Model
Surface water film thickness is one of the main factors, which affect the vehicle safety on slippery roads. Water film depth is influenced by rainfall intensity, grades, cross slopes, drainage length and pavement texture. This paper reviews the research status and makes some comparative analysis of several pavement water film depth prediction models. An experimental validation has verified and calibrated the existing water film depth prediction models results. The experimental validation of the variable in the slope water flow model has been implemented by means of a small scale physical road model in a rainfall simulator, which is constructed in a laboratory. The results of comparative analysis have shown that in the existing water film depth prediction models, the regression models predict values are more closely than mathematical-physical models. Because under different experimental conditions, the regression model calibration parameters are different. In the case of specific road characteristics for prediction of water film thickness, the model parameters can be calibrated to further improve predicting accuracy.
Validation Study of Road Surface Water Film Depth Prediction Model
Surface water film thickness is one of the main factors, which affect the vehicle safety on slippery roads. Water film depth is influenced by rainfall intensity, grades, cross slopes, drainage length and pavement texture. This paper reviews the research status and makes some comparative analysis of several pavement water film depth prediction models. An experimental validation has verified and calibrated the existing water film depth prediction models results. The experimental validation of the variable in the slope water flow model has been implemented by means of a small scale physical road model in a rainfall simulator, which is constructed in a laboratory. The results of comparative analysis have shown that in the existing water film depth prediction models, the regression models predict values are more closely than mathematical-physical models. Because under different experimental conditions, the regression model calibration parameters are different. In the case of specific road characteristics for prediction of water film thickness, the model parameters can be calibrated to further improve predicting accuracy.
Validation Study of Road Surface Water Film Depth Prediction Model
Advanced Materials Research ; 1079-1080 ; 379-385
24.12.2014
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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