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Prediction Model for Connected Voids Ratio of the Porous Asphalt Mixture

Author(s):

Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2020
Page(s): 1-11
DOI: 10.1155/2020/3760435
Abstract:

The practice showed that the difficulty to precisely predict the water permeability of the asphalt mixture by porosity creates the problem of the discrepancy between the material design and its function. And in the porous asphalt mixture, it is the connected voids ratio that truly bears the function of water permeability. However, there are many factors influencing the connected voids, and their influence laws remain to be known. Having analyzed the effects of different technical parameter on connected voids, 135°C rotation viscosity parameter was chosen in this paper to study the influences of properties of the asphalt mixture on connected voids. The equivalent width among framework gaps was quantified. The relation between the equivalent width, passing ratio of key sieve, and connected voids was discussed, and hence the prediction model for connected voids was established. The research showed that asphalt mixture with high 135°C rotation viscosity of asphalt had smaller connected voids, but the viscosity had less influence on connected voids ratio of asphalt mixture with larger equivalent width among framework gaps. The larger the equivalent width among framework gaps, the bigger the connected voids ratio. The passing rate of 2.36 mm sieve increased; both voids ratio and connected voids ratio became smaller and the difference value between them increased. Since the limitations of the composite specimens limited the prediction range, when the passing rate of 2.36 mm gradation was too small, there was a huge difference between the prediction value and the actual value. This model can predict the connected voids ratio under the stage of gradation design, thus saving the experiment time, shortening the design period, and providing some guidance to the adjustment of the raw material choosing and gradation.

Copyright: © 2020 Xiang Li and Zhaoyi He et al.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10414053
  • Published on:
    26/02/2020
  • Last updated on:
    02/06/2021
 
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