【摘要】：瀝青混合料是目前世界上用量最大的道路建設材料,從體積構成看,瀝青混合料中細集料占總體積的40%~50%。巨大的市場需求使國內天然砂資源逐年減少,機制砂替代天然砂是必然趨勢。但是我國機制砂粒徑、粒形質量參差不齊,機制砂形態(tài)質量檢測是保證混合料質量的關鍵。傳統(tǒng)振動篩分法只能實現(xiàn)機制砂粒徑級配檢測,圖像法能同時實現(xiàn)粒徑和粒形檢測。本文針對粒徑為0.6~4.75 mm的機制砂進行檢測算法研究及開發(fā)相應的檢測系統(tǒng)。設計機制砂振動分散系統(tǒng),采用CCD相機實現(xiàn)對下落砂粒進行圖像采集,為了減小機制砂表面顏色對檢測結果的影響,設計了無影背光光源系統(tǒng),研制了機制砂粒徑、粒形檢測硬件系統(tǒng)。采用高斯濾波對灰度化后的圖像進行去噪;采用最大類間方差法對濾波后的圖像進行分割得到二值圖;利用顆粒幾何特征判別圖像邊界處不完整顆粒,并進行去除;利用Hu矩特征識別并消除相鄰圖片中被重復拍攝的顆粒;通過求取顆粒凸包和凹點檢測,分離了粘連的顆粒。提出了一種新的圖像標定方法�；赩isual Studio C++、Open CV庫和Qt開發(fā)了機制砂粒徑粒形檢測軟件系統(tǒng)。對0.6~4.75 mm粒徑范圍的機制砂單級料、級配料分別進行了粒徑、粒形檢測的重復性試驗、與新帕泰克QICPIC動態(tài)顆粒圖像分析儀的精度對比試驗。實驗結果表明,對單級料和級配料的粒徑檢測最大重復性誤差為3.46%和0.51%,粒形檢測最大重復性誤差為2.97%和0.85%,顆粒形狀越趨于球形,重復性越好。與新帕泰克的粒徑結果對比,單級料與級配料兩儀器的最大偏差為7.19%和6.02%,粒形結果最大偏差為3.08%和2.42%。針對圖像法和振動篩分法機制砂粒徑檢測的差異性,提出了一種粒徑修正方法,修正后粒徑檢測精度能滿足工程實際測量需求。以國標《公路工程集料試驗規(guī)程》(JTG E42-2005)中測量細集料粒形棱角性的流動時間法為對比,對不同粒形表征參數(shù)和流動時間進行了相關性研究,得到等效橢圓長短軸比為最優(yōu)粒形表征參數(shù)。所開發(fā)的檢測系統(tǒng)能滿足機制砂粒徑、粒形實驗室檢測需求,能有效監(jiān)測機制砂質量。
[Abstract]:Asphalt mixture is the most used road construction material in the world at present. In terms of volume composition, fine aggregate in asphalt mixture accounts for 40% of the total volume. The huge market demand makes the natural sand resource decrease year by year. It is inevitable to replace the natural sand with machine-made sand. However, the particle size and grain shape of machine-made sand are not uniform in China, and the quality detection of machine-made sand is the key to ensure the quality of mixture. The traditional vibrating sieve method can only detect the particle size gradation of machine-made sand, and the image method can detect the particle size and shape at the same time. In this paper, the detection algorithm of machine-made sand with a particle size of 0.6 ~ 4.75 mm is studied and the corresponding detection system is developed. The mechanism sand vibration dispersion system is designed, and the falling sand image is collected by CCD camera. In order to reduce the influence of the surface color of the machined sand on the detection result, a non-shadow backlight light source system is designed, and the particle size of the machined sand is developed. Grain shape detection hardware system. Gao Si filter is used to denoise the grayscale image; the maximum inter-class variance method is used to segment the filtered image to obtain the binary image; the incomplete particles at the edge of the image are identified by the geometric characteristics of the particles and removed. The Hu moment feature is used to identify and eliminate the repeated shot particles in the adjacent images, and the conglutinated particles are separated by detecting the convex hull and the concave point of the particles. A new image calibration method is proposed. Based on Visual Studio C, Open CV library and Qt, a software system for particle shape detection of machine-made sand is developed. The reproducibility tests of particle size and particle shape were carried out for the single grade compound of machine-made sand with the particle size range of 0.6 ~ 4.75 mm, and the precision comparison test of the new QICPIC dynamic particle image analyzer was carried out. The experimental results show that the maximum repeatability error of particle size detection is 3.46% and 0.51% for single stage and grade proportioning, and 2.97% and 0.85% for particle shape detection. The more spherical the particle shape, the better the repeatability. Compared with the results of the particle size of neopateck, the maximum deviation of the single stage material and the grade batching is 7.19% and 6.02%, and the maximum deviation of the particle shape is 3.08% and 2.42% respectively. In view of the difference between the image method and the vibrating screen method, a particle size correction method is proposed. The precision of the modified particle size measurement can meet the needs of practical engineering measurement. Based on the flow time method for measuring fine aggregate shape and angularity in JTG E42-2005, the correlation between different particle shape characterization parameters and flow time was studied, by comparing with the flow time method in the National Standard "Highway Engineering aggregate Test Code" (JTG E42-2005). The equivalent ellipse long and short axis ratio is obtained as the optimal particle shape characterization parameter. The developed testing system can meet the requirements of particle size and particle shape laboratory testing, and can effectively monitor the quality of machined sand.
【學位授予單位】：華僑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U414;TP391.41

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