CAJ | 학술논문

由于粗集料形状的复杂性,采用机械筛分时常出现颗粒级配组成变异性大、混合料体积参数不易控制的问题,为此提出基于数字图像处理的粗集料筛分方法.将粗集料经机械筛分后随机取样,借助图像处理技术获取轮廓面积数据,通过单样本的Kolmogorov-Smirnov检验得到面积的分布规律;给出单档集料面积在一定概率水平下的分布区间,得到基于二维集料面积的粗集料筛分方法. 试验表明:数字图像处理筛分有效剔除了变异性较大的集料颗粒,粗集料分布均匀性提高,可以提高级配设计的稳定性和准确性.
유우조집료형상적복잡성,채용궤계사분시상출현과립급배조성변이성대、혼합료체적삼수불역공제적문제,위차제출기우수자도상처리적조집료사분방법.장조집료경궤계사분후수궤취양,차조도상처리기술획취륜곽면적수거,통과단양본적Kolmogorov-Smirnov검험득도면적적분포규률;급출단당집료면적재일정개솔수평하적분포구간,득도기우이유집료면적적조집료사분방법. 시험표명:수자도상처리사분유효척제료변이성교대적집료과립,조집료분포균균성제고,가이제고급배설계적은정성화준학성.
Due to the complexity of the coarse aggregate shape, the use of mechanical sieving often results in such problems as the variability of coarse aggregate particle gradation composition, difficulty in controlling mixture volume parameters. This study puts forward the coarse aggregate sieving method based on digital image processing to obtain area data of the outline. After random sampling of the coarse aggregate sieved mechanically, it obtains distribution law of aggregate area with one sample Kolmogorov-Smirnov test. By illustrating the distribution range at a certain probability level, it develops the coarse aggregate sieving method based on two-dimensional area. Experiments show that coarse aggregate of larger variability is removed effectively and discrete is reduced after the digital image sieving. The proposed method can improve the stability and accuracy of grading design.