基于数字图像处理技术沥青混合料空隙及级配检测的研究

发布时间：2018-02-17 02:10

本文关键词： 沥青混合料 CT技术伪三维数字图像处理技术空隙级配检测　出处：《吉林大学》2014年硕士论文　论文类型：学位论文

【摘要】：沥青路面因其便于施工与维护以及良好的行车舒适性等优点已经被广泛的应用到路面工程中，沥青路面主要由沥青混合料构成。沥青胶浆和矿料是沥青混合料的主要组成部分，其路用性能受多种因素的影响；其中，沥青混合料的空隙率及矿料级配是影响其路用性能的主要因素。因此，沥青混合料的空隙率及矿料级配的检测是对其质量控制的重要环节。然而，传统的检测方法存在着按体积设计按质量检验的矛盾且容易受到人为因素的影响。随着计算机技术以及图像获取技术的发展，基于数字图像处理技术沥青混合料内部的细观研究逐渐受到学者们的青睐，利用该方法对沥青混合料的空隙及矿料级配检测的研究也更多的受到研究者的重视。本文利用CT技术对沥青混合料马歇尔试件内部的切面图像进行获取；利用数字图像处理技术对其切面图像进行处理，分离出沥青混合料的三相物质：集料、沥青胶浆和空隙；进而对沥青混合料的空隙及矿料级配检测进行研究。在对空隙的研究中，主要对沥青混合料马歇尔试件空隙率的测定，内部空隙的分布及不同大小空隙所占的比例进行研究。研究表明：基于CT技术及DIP法对沥青混合料马歇尔试件空隙率检测的精度较高，受人为因素的影响较少；试件内部空隙的分布存在着不均匀性；小空隙在空隙面积中所占比例较大。在对矿料级配检测的研究得到：本文所采用的CT技术所获取的沥青混合料马歇尔试件的“伪三维”切面图像可以较好的对粗集料进行识别，但对粒径较小的细集料的识别效果较差；对40副以上的切面图像进行统计可以得到粗集料稳定的识别结果；在对稳定的识别结果进行体积换算后，得到的级配识别结果中：大于4.75mm以上的粗集料识别效果良好，精度较高；粒径在2.36~4.75mm范围内的集料由于受到图像分割的影响需进行简单的修正；修正后的粗集料的识别结果良好，误差在5%以内，可以满足工程上的要求。
[Abstract]:Asphalt pavement has been widely used in pavement engineering because of its advantages such as easy construction and maintenance and good driving comfort. Asphalt pavement is mainly composed of asphalt mixture. Asphalt mortar and mineral aggregate are the main components of asphalt mixture. The pavement performance is affected by many factors, among which, the porosity of asphalt mixture and the gradation of mineral aggregate are the main factors affecting its road performance. The void ratio of asphalt mixture and the detection of the gradation of mineral aggregate are the important links in the quality control of asphalt mixture. However, The traditional detection method has the contradiction of designing according to volume and quality, and is easily influenced by human factors. With the development of computer technology and image acquisition technology, The mesoscopic research of asphalt mixture based on digital image processing technology has gradually been favored by scholars. The research on the void and gradation of asphalt mixture using this method is also paid more attention by researchers. In this paper, CT technology is used to obtain the cut image of Marshall sample of asphalt mixture, digital image processing technology is used to process the image, and the three-phase materials of asphalt mixture, such as aggregate, asphalt paste and void, are separated. In the study of the void, the porosity of the Marshall test piece of asphalt mixture is mainly measured. The distribution of internal voids and the proportion of different sizes of voids are studied. The results show that the accuracy of voidage detection of asphalt mixture Marshall specimen based on CT technique and DIP method is high and is less influenced by human factors. There is inhomogeneity in the distribution of the voids in the specimen. The small voids account for a large proportion of the void area. In the study of mineral gradation detection, it is concluded that the "pseudo-3D" section image of the asphalt mixture Marshall specimen obtained by CT technology in this paper can better identify coarse aggregate. However, the recognition effect of fine aggregate with smaller particle size is poor. The stable recognition results of coarse aggregate can be obtained by statistical analysis of 40 pairs of cross-section images. After the volume conversion of the stable recognition results, Among the gradation recognition results obtained, the coarse aggregate above 4.75mm has good recognition effect and high precision, and the aggregate with the diameter of 2.36 ~ 4.75mm needs to be modified simply because of the influence of image segmentation, and the recognition result of the modified coarse aggregate is good. The error is less than 5%, which can meet the engineering requirements.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U414

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