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Scientific Papers

Surface drainage evaluation of asphalt pavement using a new analytical water film depth model

ORCID Icon, , &
Pages 1985-2004 | Received 03 May 2018, Accepted 25 Feb 2019, Published online: 20 Mar 2019
 

Abstract

Water film formed on the pavement surface would cause potential hydroplaning, so evaluation of pavement drainage is critical in the pavement safety analysis. However, the widely used water film depth (WFD) models are empirical models. The empirical data contributing to model development and the storm design of the empirical models limit their application scope and reduce their accuracy. This study develops an analytical WFD model by simulating the dynamic of the sheet flow on the pavement surface. Pavement permeability, texture, and geometry combined with rainfall coefficient are considered as the intervening factors on WFD. The proposed new analytical WFD model assists in capturing the highest intensity and covers the impacts of accumulated rainwater on WFDs. In order to acquire the rainfall data and the field WFDs data for the model application and validation, Pavement Drainage Measuring Instrument is developed in this study. Indeed, pavement texture, pavement slope, and pavement permeability are measured by LS-40 surface texture analyser, SurPro3500 walking profiler, and seepage instrument, respectively. Subsequently, a case study of WFD prediction on 10 test sites is provided based on the new analytical WFD model and the two widely used WFD models (PAVDRN and Gallaway model). Finally, a validation test is conducted by comparing the predicted WFDs with the field measured WFD (reference). The results from Kendall correlation method illustrate that the proposed new analytical WFD model (correlation coefficient of 0.7433) is more accurate in WFDs prediction for asphalt pavement than the PAVDRN model (correlation coefficient of 0.3343) and the Gallaway model (correlation coefficient of 0.4201). The new analytical WFD model can be applied by transportation agencies for pavement drainage evaluation.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China [grant number 2018YFB1201601]; the Fujian Natural Science Foundations, China [grant number 2017J01682]; and the Outstanding Young Scientific Research Talent Program of Fujian Agriculture and Forestry University [grant number XJQ2018007].

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