Real-time monitoring of asphalt mixture mixing process
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摘要: 为控制道路施工过程中沥青混合料的拌和质量与拌和状态, 提出一种以非介入方式利用模板匹配识别算法实时提取骨料、粉料、沥青质量数据、拌和时间及温度等沥青混合料主成分数据信息的方法, 根据识别到的沥青混合料数据信息建立了数据采集与传输的时序逻辑关系; 在WEB监控中心下可视化显示了沥青混合料配合比误差、级配误差、拌和时间和温度等关键信息, 并利用这些多模态信息融合策略评价了沥青混合料的拌和质量; 根据施工过程中沥青混合料类型的先验知识分析了混合料数据的动态变化, 在无人工干预的情况下自动识别了实时生产的沥青混合料类型; 建立了骨料数据的模型分布, 并结合拌和时间判断拌和设备的运行和筛分状态; 存储实时接收到的数据, 实现了沥青混合料历史数据跨时间查询和成本评判。研究结果表明: 利用模板匹配识别算法采集沥青混合料字符数据时间为4.9 ms, 识别准确率达100%, 满足了施工中沥青混合料拌和数据采集时间间隔小于0.02 s的要求, 实现了施工过程中沥青混合料数据的连续检测、自动识别、实时跟踪和可视化监控; 当沥青混合料质量不合格或拌和设备出现故障时可实时预警, 为综合评价沥青混合料拌和过程与实时掌控沥青混合料拌和质量提供了依据。Abstract: To control the mixing quality and mixing state of asphalt mixture during the road construction process, a method based on the template matching recognition algorithm in a non-intrusive manner was proposed to extract the asphalt mixture principal component data, such as aggregate, powder, asphalt quality data, mixing time, and temperature in real-time. Based on the identified asphalt mixture data information, the time sequence logic relationship between data acquisition and transmission was established. The WEB monitoring center visually displayed the key information such as the asphalt mixture ratio error, gradation error, mixing time, and temperature. The multimodal information fusion strategy was used to evaluate the asphalt mixture's mixing quality. Based on the prior knowledge of asphalt mixture type during the construction process, the dynamic change of mixture data was analyzed, and the type of asphalt mixture produced in real-time was automatically identified without the manual intervention. The running and screening statuses of mixing equipment were determined by the established model distribution of aggregate data and the mixing time. The historical data were queried across time and the construction cost was assessed according to the stored real-time received data. Research result shows that the time for collecting the character data of asphalt mixture is 4.9 ms by using the template matching recognition algorithm, and the recognition accuracy rate is up to 100%. It meets the time interval requirement that the mixing data collection of asphalt mixture during the construction is less than 0.02 s. The continuous detection, automatic identification, real-time tracking and visual monitoring on asphalt mixture data during the construction process are realized. The real-time warning is realized when the quality of asphalt mixture is unqualified or the mixing equipment fails. It provides a basis for the comprehensive evaluation of mixing process and the real-time control of mixing quality for asphalt mixture.
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图 3 沥青混合料拌和设备控制图像信号
Figure 3. Control image signals of asphalt mixture mixing equipment
图 5 沥青混合料数据的采集与识别流程
Figure 5. Flow of collection and identification on asphalt mixture data
图 6 按骨料质量变化采集的时序逻辑
Figure 6. Collected time sequence logic according to change of aggregate mass
图 7 骨料1的配合比误差曲线和级配曲线
Figure 7. Mixture ratio error curves and gradation curves of aggregate 1
表 1 拌和过程中主要因素误差范围
Table 1. Error ranges of main factors during mixing process
拌和生产过程主要因素 误差范围 骨料配合比/% 3~5 粉料配合比/% 3~5 油石比/% 0.3 生产级配 施工上下限范围内 出料温度 不同沥青标号规定的出料温度误差范围内 拌和时间/s ≥45 
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表 2 沥青混合料数据的识别结果
Table 2. Identification results of asphalt mixture data
算法 准确率/% 耗时/ms 模板匹配识别 100.00 4.9 BP神经网络 99.61 3.2 KNN 99.48 2.9
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表 3 沥青混合料配合比参数
Table 3. Mixing ratio parameters of asphalt mixture
材料规格 骨料4 骨料3 骨料2 骨料1 石灰 矿粉 目标配合比/% 24.0 12.0 20.0 40.0 1.5 2.5 生产配合比/% 22.7 11.1 19.5 43.4 1.4 2.3
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表 4 盘号、时间、温度、骨料和粉料数据
Table 4. Data of plate number, time, temperature, aggregate and powder
盘号 时刻 拌和时间/s 温度/℃ 骨料1/kg 骨料2/kg 骨料3/kg 骨料4/kg 骨料5/kg 粉料1/kg 粉料2/kg 45 7:47:02 62 173 640 732 677 1 311 305 58.1 133.1 46 7:48:11 69 172 626 663 691 1 172 310 65.2 131.2 47 8:17:41 1 770 171 557 735 679 1 211 305 58.8 131.3 48 8:57:00 2 359 171 527 605 598 1 155 282 58.8 120.2 49 9:02:34 334 155 556 310 721 1 949 139.9 221.5 50 9:07:09 275 154 273 307 705 1 961 157.9 202.3 51 9:08:18 69 155 300 300 709 1 972 146.9 204.3 52 9:10:56 158 155 250 293 655 1 982 151.3 207.6 156 12:38:38 81 161 278 264 685 1 978 148.7 203.8 157 12:59:06 1 228 160 295 292 698 1 949 154.1 207.7 158 13:00:14 68 160 285 294 694 1 969 150.1 207.2
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[1] LU Yang, WANG Lin-bing. Nanoscale modelling of mechanical properties of asphalt-aggregate interface under tensile loading[J]. International Journal of Pavement Engineering, 2010, 11(5): 393-401. doi: 10.1080/10298436.2010.488733 [2] MHASKE D, SABARISH N B. Microwave reflectometry study for quality monitoring of asphalt concrete[J]. Procedia Computer Science, 2017, 115: 779-785. doi: 10.1016/j.procs.2017.09.173 [3] ZHENG Jian-long, LI Hong-zhi. Performance contrast of asphalt macadam based on different gradation[C]∥IEEE. 2010 International Conference on Measuring Technology and Mechatronics Automation. New York: IEEE, 2010: 876-878. [4] SEBAALY H, VARMA S, MAINA J W. Optimizing asphalt mix design process using artificial neural network and genetic algorithm[J]. Construction and Building Materials, 2018, 168: 660-670. doi: 10.1016/j.conbuildmat.2018.02.118 [5] KASSEM E, LIU W, SCULLION T, et al. Development of compaction monitoring system for asphalt pavements[J]. Construction and Building Materials, 2015, 96: 334-345. doi: 10.1016/j.conbuildmat.2015.07.041 [6] 刘东海, 吴优. 路面摊铺厚度的连续检测与可视化实时监控[J]. 中国公路学报, 2017, 30(11): 163-169. doi: 10.3969/j.issn.1001-7372.2017.11.018LIU Dong-hai, WU You. Continuous measuring and real-time visualization monitoring of pavement lift thickness in highway construction[J]. China Journal of Highway and Transport, 2017, 30(11): 163-169. (in Chinese). doi: 10.3969/j.issn.1001-7372.2017.11.018 [7] SHANGGUAN P, AL-QADI I, COENEN A, et al. Algorithm development for the application of ground-penetrating radar on asphalt pavement compaction monitoring[J]. International Journal of Pavement Engineering, 2014, 17(3): 189-200. [8] ZHU Xing-yi, BAI Shun-jie, XUE Gen-ping, et al. Assessment of compaction quality of multi-layer pavement structure based on intelligent compaction technology[J]. Construction and Building Materials, 2018, 161: 316-329. doi: 10.1016/j.conbuildmat.2017.11.139 [9] CHA Y J, CHOI W, BUYUKOZTURK O. Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361-378. doi: 10.1111/mice.12263 [10] 沙爱民, 童峥, 高杰. 基于卷积神经网络的路表病害识别与测量[J]. 中国公路学报, 2018, 31(1): 1-10. doi: 10.3969/j.issn.1001-7372.2018.01.001SHA Ai-min, TONG Zheng, GAO Jie. Recognition and measurement of pavement disasters based on convolutional neural networks[J]. China Journal of Highway and Transport, 2018, 31(1): 1-10. (in Chinese). doi: 10.3969/j.issn.1001-7372.2018.01.001 [11] 陈燕娟, 高建明, 陈华鑫. 基于表面能理论的沥青-集料体系的粘附特性研究[J]. 东南大学学报(自然科学版), 2014, 44(1): 183-187. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201401034.htmCHEN Yan-juan, GAO Jian-ming, CHEN Hua-xin. Research on adhesion in asphalt-aggregate systems based on surface energy theory[J]. Journal of Southeast University (Natural Science Edition), 2014, 44(1): 183-187. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201401034.htm [12] ZAUMANIS M, HARITONOVS V. Long term monitoring of full scale pavement test section with eight different asphalt wearing courses[J]. Materials and Structures, 2016, 49(5): 1817-1828. doi: 10.1617/s11527-015-0614-6 [13] LI Xin, CHANG Feng-nian, LI Yan-wei, et al. Mesomechanics simulation of micro-crack extension process for asphalt mixture[J]. Journal of Traffic and Transportation Engineering, 2011, 11(6): 1-9. [14] SIVILEVICIUS H, SUKEVICIUS S. Manufacturing technologies and dynamics of hot-mix asphalt mixture production[J]. Journal of Civil Engineering and Management, 2009, 15(2): 169-179. doi: 10.3846/1392-3730.2009.15.169-179 [15] KABADURMUS O, PATHAK O, SMITH J S, et al. A simulation methodology for online process control of hot mix asphalt (HMA) production[C]∥IEEE. 2010 Winter Simulation Conference. New York: IEEE, 2010: 1522-1533. [16] OLIVEIRA H, CORREIA P L. Automatic road crack detection and characterization[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 14(1): 155-168. [17] 李正中, 周卫峰, 庞佳宇, 等. 厂拌沥青混合料质量实时监控系统的研发及功能实现[J]. 中外公路, 2012, 32(3): 336-339. doi: 10.3969/j.issn.1671-2579.2012.03.081LI Zheng-zhong, ZHOU Wei-feng, PANG Jia-yu. Development and function realization of real-time monitoring system for mixed asphalt mixture quality[J]. Journal of China and Foreign Highway, 2012, 32(3): 336-339. (in Chinese). doi: 10.3969/j.issn.1671-2579.2012.03.081 [18] 董刚, 刘义彬, 郑南翔. 沥青路面施工过程质量信息实时监控系统研究[J]. 公路交通科技, 2015, 32(11): 27-32. doi: 10.3969/j.issn.1002-0268.2015.11.005DONG Gang, LIU Yi-bin, ZHENG Nan-xiang. Study on real-time monitoring system of asphalt pavement construction process quality information[J]. Journal of Highway and Transportation Research and Development, 2015, 32(11): 27-32. (in Chinese). doi: 10.3969/j.issn.1002-0268.2015.11.005 [19] 宋胜利, 肖翀宇, 李万莉, 等. 基于B/S的沥青混合料搅拌设备远程监控系统[J]. 南京航空航天大学学报, 2005, 37(S1): 49-52. https://www.cnki.com.cn/Article/CJFDTOTAL-NJHK2005S1010.htmSONG Sheng-li, XIAO Chong-yu, LI Wan-li. et al. Remote monitoring system for bituminous concrete stirring plant based on B/S model[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2005, 37(S1): 49-52. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-NJHK2005S1010.htm [20] 潘一凡, 张显峰, 童庆禧, 等. 公路路面质量遥感监测研究进展[J]. 遥感学报, 2017, 21(5): 796-811. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201705014.htmPAN Yi-fan, ZHANG Xian-feng, TONG Qing-xi, et al. Progress on road pavement condition detection based on remote sensing monitoring[J]. Journal of Remote Sensing, 2017, 21(5): 796-811. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201705014.htm [21] ALI A, ABBAS A, NAZZAL M, et al. Effect of temperature reduction, foaming water content, and aggregate moisture content on performance of foamed warm mix asphalt[J]. Construction and Building Materials, 2013, 48: 1058-1066. doi: 10.1016/j.conbuildmat.2013.07.081 [22] MANSOUR T N, PUTMAN B J. Influence of aggregate gradation on the performance properties of porous asphalt mixtures[J]. Journal of Materials in Civil Engineering, 2012, 25(2): 281-288. [23] RODRIUEZ-ALLOZA A M, GALLEGO J, PEREZ I, et al. High and low temperature properties of crumb rubber modified binders containing warm mix asphalt additives[J]. Construction and Building Materials, 2014, 53: 460-466. doi: 10.1016/j.conbuildmat.2013.12.026 [24] BRAZIUNAS J, SIVILEVICIUS H. The bitumen batching system's modernization and its effective analysis at the asphalt mixing plant[J]. Transport, 2010, 25(3): 325-335. doi: 10.3846/transport.2010.40 [25] WEST R C. Development of rapid QC procedures for evaluation of HMA properties during production[J]. National Center for Asphalt Technology Report, 2005, 5(1): 1-22. [26] KHALIL M I. Car plate recognition using the template matching method[J]. International Journal of Computer Theory and Engineering, 2010, 2(5): 683-687. [27] DING Shi-fei, SU Chun-yang, YU Jun-zhao. An optimizing BP neural network algorithm based on genetic algorithm[J]. Artificial Intelligence Review, 2011, 36(2): 153-162. doi: 10.1007/s10462-011-9208-z [28] ZHANG Min-ling, ZHOU Zhi-hua. ML-KNN: a lazy learning approach to multi-label learning[J]. Pattern Recognition, 2007, 40(7): 2038-2048. [29] LU C J, SHAO Y E, LI P H. Mixture control chart patterns recognition using independent component analysis and support vector machine[J]. Neurocomputing, 2011, 74(11): 1908-1914. [30] ZHANG Min, HEITZMAN M, SMITH A E. Improving hot mix asphalt production using computer simulation and real time optimization[J]. Journal of Computing in Civil Engineering, 2013, 28(3): 04014011-1-8. [31] GIACOMO D M. MySQL: lessons learned on a digital library[J]. IEEE Software, 2005, 22(3): 10-13. doi: 10.1109/MS.2005.71 [32] 延西利, 田辉黎, 延喜乐, 等. 沥青混合料的变速拌和功率测试与拌和流变模型[J]. 交通运输工程学报, 2016, 16(3): 1-7. doi: 10.3969/j.issn.1671-1637.2016.03.001YAN Xi-li, TIAN Hui-li, YAN Xi-le, et al. Mixing power measurement at different mixing velocities and mixing rheological model of bituminous mixture[J]. Journal of Traffic and Transportation Engineering, 2016, 16(3): 1-7. (in Chinese). doi: 10.3969/j.issn.1671-1637.2016.03.001 [33] 张翠红, 焦生杰, 曹学鹏, 等. 水泥乳化沥青混合料施工和易性评价方法及影响因素[J]. 长安大学学报(自然科学版), 2018, 38(1): 41-48. https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL201801007.htmZHANG Cui-hong, JIAO Sheng-jie, CAO Xue-peng, et al. Evaluation method and influencing factors for construction workability of cement-emulsified asphalt mixture[J]. Journal of Chang'an University (Natural Science Edition), 2018, 38(1): 41-48. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL201801007.htm -
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