摘要自密实混凝土（self-compacting concrete，SCC）的工作性能反映在其搅拌过程中，可以通过分析搅拌图像对工作性能的好坏进行初步的判断。该文利用双轴搅拌机搅拌了具备不同工作性能的8组SCC，并利用智能手机录制视频，采集了全部搅拌过程的图像信息。然后，从图像序列中选取感兴趣的图像和感兴趣的区域（region of interest，ROI），利用图像分析手段进行量化的比较。搅拌机指定叶片与转轴呈45°时的图像选为感兴趣的图像。ROI中的SCC轮廓线形状可以用作区分SCC的SF值的视觉特征。利用轮廓线上横坐标为300像素的点的纵坐标Y300，可以提炼轮廓线特征，并用于推算SCC的SF值。研究结果表明：Y300与SCC的SF值之间存在较好的相关关系，推算结果误差较小，可以用于开发基于双轴搅拌机的SCC工作性能实时检测系统。
Abstract：The movement of self-compacting concrete (SCC) during mixing reflects its workability. Therefore, the SCC workability can be quantified by analyzing mixing images. In this study, eight concrete specimens with different workabilities were produced in a twin-shaft mixer. A smart phone was used to record all the mixing processes. Afterwards, images of interest and regions of interest (ROI) were selected from the image sequences for quantitative comparisons. The image of interest was the moment when a blade arm formed a 45° angle with the shaft before the mixer stopped. The SCC contour within the ROI was used as an indicator to indicate the SCC workability. The Y coordinate of the point with an X coordinate of 300 pixels (Y300) was used to describe the SCC contour and to estimate SF. The results show that Y300 is directly related to SF with a small relative error. This method can be used in a real-time monitoring system to estimate the SCC workability during mixing.
丁仲聪, 安雪晖. 基于双轴图像的自密实混凝土工作性能分析[J]. 清华大学学报（自然科学版）, 2018, 58(11): 979-985.
DING Zhongcong, AN Xuehui. Analysis of self-compacting concrete workability based on twin-shaft mixing images. Journal of Tsinghua University(Science and Technology), 2018, 58(11): 979-985.
[1] OKAMURA H, OUCHI M. Self-compacting concrete[J]. Journal of Advanced Concrete Technology, 2003, 1(1):1-5. [2] AN X H, WU Q, JIN F, et al. Rock-filled concrete, the new norm of SCC in hydraulic engineering in China[J]. Cement and Concrete Composites, 2014, 54:89-99. [3] WU Q, AN X H. Development of a mix design method for SCC based on the rheological characteristics of paste[J]. Construction and Building Materials, 2014, 53:642-651. [4] FERRARIS C, BROWER L, OZYILDIRIM C, et al. Workability of self-compacting concrete[C]//PCI/FHWA/FIB International Symposium on High Performance Concrete. Orlando Florida, United States:NIST, 2000:398-407. [5] 李书阳, 沈乔楠, 安雪晖, 等. 基于增强现实的混凝土坍落扩展度测量[J]. 清华大学学报(自然科学版), 2012, 52(6):809-813. LI S Y, SHEN Q N, AN X H, et al. Slump flow measurements based on augmented reality[J]. Journal of Tsinghua University (Science and Technology), 2012, 52(6):809-813. (in Chinese) [6] DAUMANN B, NIRSCHL H. Assessment of the mixing efficiency of solid mixtures by means of image analysis[J]. Powder Technology, 2008, 182(3):415-423. [7] JUEZ J M, ARTONI R, CAZACLIU B. Monitoring of concrete mixing evolution using image analysis[J]. Powder Technology, 2017, 305:477-487. [8] BERTHIAUX H, MOSOROV V, TOMCZAK L, et al. Principal component analysis for characterising homogeneity in powder mixing using image processing techniques[J]. Chemical Engineering and Processing:Process Intensification, 2006, 45(5):397-403. [9] LI S Y, AN X H. Method for estimating workability of self-compacting concrete using mixing process images[J]. Computers and Concrete, 2014, 13(6):781-798. [10] LI L G, KWAN A K H. Concrete mix design based on water film thickness and paste film thickness[J]. Cement and Concrete Composites, 2013, 39:33-42. [11] GIRISH S, RANGANATH R V, VENGALA J. Influence of powder and paste on flow properties of SCC[J]. Construction and Building Materials, 2010, 24(12):2481-2488. [12] CAZACLIU B. In-mixer measurements for describing mixture evolution during concrete mixing[J]. Chemical Engineering Research and Design, 2008, 86(12):1423-1433. [13] CARRON T, LAMBERT P. Color edge detector using jointly hue, saturation and intensity[C]//Proceedings of the 1st International Conference on Image Processing. Austin, USA:IEEE, 1994. [14] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979(1):62-66. [15] HADDON J F, BOYCE J F. Image segmentation by unifying region and boundary information[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(10):929-948.
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