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HUANG Qi, ZHAO Xin, LEI Yu, SHA Ai-min. Relationship between gradation type and segregation degree for asphalt mixture[J]. Journal of Traffic and Transportation Engineering, 2009, 9(2): 1-6. doi: 10.19818/j.cnki.1671-1637.2009.02.001
Citation: HUANG Qi, ZHAO Xin, LEI Yu, SHA Ai-min. Relationship between gradation type and segregation degree for asphalt mixture[J]. Journal of Traffic and Transportation Engineering, 2009, 9(2): 1-6. doi: 10.19818/j.cnki.1671-1637.2009.02.001
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Relationship between gradation type and segregation degree for asphalt mixture

doi: 10.19818/j.cnki.1671-1637.2009.02.001
  • 1.

    Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    CCCC First Highway Consultants Co., Ltd., Xi'an 710075, Shaanxi, China

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  • Author Bio:

    HUANG Qi(1970-), female, doctoral student, +86-371-68980337, hq66138@sina.com

    SHA Ai-min(1964-), male, profe sso r, PhD, +86-29-82334015, aiminsha@263.net

  • Received Date: 2009-02-16
  • Publish Date: 2009-04-25
    Abstract
  • Six curves of gradation types with different properties were designed, the gradation constitutes, texture depthes and densities of asphalt mixtures with different gradations at five different positions were detected, and the segregation degrees and rules of asphalt mixtures in construction process were investigated. By studying the relationship between the mean squares of test data and the fractal dimensions of aggregates, a linear regression model of the relationship were established. Using the model, the gradation of asphalt mixture was evaluated, and the degree of segregation was forecasted. Analysis result shows that the mixtures with different segregation degrees from big to small are as follows: anti-S type, discontinuous type, micro-arch type above the maximum density curve, S type, maximum density curve type and micro-arch type under the maximum density curve. The probability value of F(1, 5) significance test is 0.013, which is significantly less than the significance level of 0.05, so the linear model is effective.

     

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