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摘要: 为了提高集料棱角性评价的准确性, 提出了集料三维棱角性计算方法; 基于CT技术和三维重建技术, 对集料的CT图像进行增强和锐化滤镜处理, 以突显沥青混合料中的集料; 对增强后的CT图像进行灰度阈值计算与灰度划分, 采用MIMICS重构了马歇尔试件中集料的三维模型; 提出了集料粗糙度与球形度的评价指标, 依据集料三维模型评价了集料棱角性, 并分析了三维模型重建的影响因素。计算结果表明: 在AC-16马歇尔试件中, 集料、沥青和孔隙的灰度分别为101.32~170.14、4.32~101.32和0~4.32, 因此, 采用图像增强和锐化滤镜处理可以突显CT图像中的集料, 增强集料三维重建的准确性; 采用2pixels×2pixels、3pixels×3pixels锐化滤镜计算球形度标准差为0.000 7, 而采用5pixels×5pixels、6pixels×6pixels、7pixels×7pixels锐化滤镜计算得到的球形度标准差为0.042 3, 因此, 应当采用2pixels×2pixels或3pixels×3pixels锐化滤镜处理CT图像, 以确保球形度计算结果波动小; 采用50、70个·mm-3采样点密度计算粗糙度的标准差为0.001 6, 而采用5、15、25个·mm-3采样点密度计算粗糙度的标准差为0.034 9, 因此, 应当采用50~70个·mm-3采样点密度来保证集料三维模型精确地反映集料的真实状态; 采用5个CT截面图像计算的二维球形度和粗糙度的标准差为0.012 1~0.048 2, 存在较大变异性和偏差, 而采用基于三维集料模型的粗糙度计算方法得到集料15的粗糙度分别为0.991 2、1.032 1、0.974 2、1.075 1、1.043 2, 集料1~5的平均二维粗糙度分别为0.994 1、1.023 9、0.988 3、1.097 5、1.060 8, 两者基本一致。可见, 基于三维集料模型的粗糙度和球形度计算方法充分考虑了集料的棱角性, 计算结果不受CT截面的影响, 计算结果不存在变异和偏差。Abstract: To improve the evaluation accuracy of aggregate angularity, a computational method of3 D aggregate angularity was put forward.Based on CT technology and 3 D reconstruction technology, image intensification and sharpen filter were used to clearly display the aggregates in aggregate CT images, and the gray thresholds of enhanced CT images were calculated and divided.MIMICS was used to reconstruct the 3 D models of aggregates in Marshal specimens.The roughness degree and sphericity degree of aggregate were put forward and taken as evaluation indexes to evaluate aggregate angularity based on the 3 D model.The influence factorsof 3 D model reconstruction were analyzed.Computational result shows the gray values of aggregate, asphalt and pore in AC-16 Marshall specimens are 101.32-170.14, 4.32-101.32, and0-4.32, respectively, so, image intensification and sharpen filter can clearly display aggregates in CT images and improve the 3 Dreconstruction accuracy of aggregate.The computational standard deviation of sphericity degrees is 0.000 7 when the CT images are dealt with by 2 pixels×2 pixels and 3 pixels×3 pixels sharpen filters, and the standard deviation of sphericity degrees is 0.042 3 when the CT images are dealt with by 5 pixels×5 pixels, 6 pixels×6 pixels and 7 pixels×7 pixels sharpen filters.Therefore, 2 pixels×2 pixels or 3 pixels×3 pixels sharpen filter should be adopted to dealt with the CT images in order to ensure the low fluctuation of computed sphericity degree.When sampling point densities are 50 and 70 per cubic millimeter, the standard deviation of roughness degrees is 0.001 6, but when sampling point densities are 5, 15 and 25 per cubic millimeter, the standard deviation of roughness degrees is 0.034 9, so, the density range of sampling points in CT images should be 50-70 per cubic millimeter to ensure that the 3 D models can reflect the real shapes of aggregates precisely.The standard deviations of 2 D sphericity degrees and roughness degrees based on 5 CT cross sections are 0.012 1-0.048 2, which reflects the larger variability and deviation.However, the roughness degrees of aggregates 1-5 calculated by the method based on the 3 D models are 0.991 2, 1.032 1, 0.974 2, 1.075 1, 1.043 2, respectively, and the corresponding average 2 Droughness degrees of aggregates 1-5 are 0.994 1, 1.023 9, 0.988 3, 1.097 5 and 1.060 8, respectively, which shows the 3 Dand 2 Droughness degrees are basically same.Obviously, the computational method based on the 3 D model used to calculate the roughness degree and sphericity degree fully considers the 3 D angularity of aggregate, the calculation result is not affected by the selected CT cross sections, and there are no calculation variability and deviation.
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Key words:
- pavement material /
- aggregate angularity /
- CT technology /
- 3Dreconstruction /
- sharpen filter /
- gray threshold
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图 7 滤镜尺寸对球形度计算结果的影响
Figure 7. Influences of filter sizes on computation results of sphericity degrees
图 8 滤镜尺寸对粗糙度计算结果的影响
Figure 8. Influences of filter sizes on computation results of roughness degrees
图 11 采样点密度对球形度计算结果的影响
Figure 11. Influences of sample point densities on computation results of sphericity degrees
图 12 采样点密度对粗糙度计算结果的影响
Figure 12. Influences of sample point densities on computation results of roughness degrees
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