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摘要: 调整出具有不同特征的6种级配曲线, 施工中在5个不同位置取样, 检测不同级配类型混合料的级配组成、构造深度和密度, 研究了各种级配混合料在生产及施工过程中发生离析的程度与规律。分析了检测数据的平均组间离差平方和及集料分形维数之间的关系, 建立了二者之间的线性回归模型, 进行了混合料级配的评估与离析程度的预测。分析结果表明: 各种级配混合料离析程度由大到小分别为反S型、间断型、最大密度曲线之上微弓型、S型、最大密度曲线型与最大密度曲线之下微弓型; 对回归方程进行F(1, 5)显著性检验, 其概率值为0.013, 小于显著水平0.05, 说明该线性模型有效。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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Key words:
- pavement material /
- asphalt mixture /
- fractal dimension /
- gradation type /
- segregation degree
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表 1 横向分布方差分析
Table 1. Variance analysis of transverse distribution
级配 各点位铺砂直径纵向平均值/cm 纵向各组均方离差 组间 组内 0# 20.90 20.48 17.25 47.55 2.49 1# 19.75 20.49 17.41 25.79 5.27 2# 23.70 24.40 18.50 103.34 8.23 3# 27.85 27.65 22.58 89.37 10.00 4# 25.40 25.30 19.10 116.46 7.04 5# 17.82 17.29 16.92 2.64 3.88 6# 17.23 17.91 15.56 17.59 1.29 
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表 2 各级配特征参数
Table 2. Gradation character parameters
级配 D D2 D1 0# 2.424 0 2.431 1 2.446 5 1# 2.429 2 2.442 3 2.469 3 2# 2.455 9 2.523 6 2.392 8 3# 2.444 1 2.553 7 2.367 6 4# 2.460 0 2.430 8 2.395 7 5# 2.443 9 2.394 8 2.446 9 6# 2.412 9 2.343 2 2.484 8
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表 3 组间均方离差与分形维数相关性分析
Table 3. Correlation analysis of mean square and fractal dimension
参数 M D D2 D1 M 泊松相关性 1 0.729 0.677 -0.859* 显著性(双尾数) 0.063 0.095 0.013 样本数 7 7 7 7 D 泊松相关性 0.729 1 0.568 -0.805* 显著性(双尾数) 0.063 0.184 0.029 样本数 7 7 7 7 D2 泊松相关性 0.677 0.568 1 -0.841 显著性(双尾数) 0.095 0.184 0.018 样本数 7 7 7 7 D1 泊松相关性 -0.859* -0.805* -0.841* 1 显著性(双尾数) 0.013 0.029 0.018 样本数 7 7 7 7 注: α=0.05水平下, 相关性显著(双尾数检验)。
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表 4 回归方程显著性检验
Table 4. Significance test of regression equation
参数 平方和 D1 均方值 F值 显著性 回归值 0.009 1 0.009 14.080 0.013 误差 0.003 5 0.001 合计 0.012 6
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