Real-time monitoring of asphalt mixture mixing process

被引:0
|
作者
Chen Y. [1 ,2 ]
Ge L.-P. [3 ]
Song H.-S. [1 ]
机构
[1] School of Information Engineering, Chang'an University, Xi'an, 710064, Shaanxi
[2] School of Foreign Studies, Chang'an University, Xi'an, 710064, Shaanxi
[3] Sinohydro Bureau 7 Co., Ltd., Chengdu, 610213, Sichuan
关键词
Asphalt mixture; Mixing quality; Multimodal; Pavement engineering; Real-time monitoring; Template matching recognition;
D O I
10.19818/j.cnki.1671-1637.2019.06.003
中图分类号
学科分类号
摘要
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. 4 tabs, 11 figs, 33 refs. © 2019, Editorial Department of Journal of Traffic and Transportation Engineering. All right reserved.
引用
收藏
页码:27 / 36
页数:9
相关论文
共 33 条
  • [1] Lu Y., Wang L.-B., Nanoscale modelling of mechanical properties of asphalt-aggregate interface under tensile loading, International Journal of Pavement Engineering, 11, 5, pp. 393-401, (2010)
  • [2] Mhaske D., Sabarish N.B., Microwave reflectometry study for quality monitoring of asphalt concrete, Procedia Computer Science, 115, pp. 779-785, (2017)
  • [3] Zheng J.-L., Li H.-Z., Performance contrast of asphalt macadam based on different gradation, 2010 International Conference on Measuring Technology and Mechatronics Automation, pp. 876-878, (2010)
  • [4] Sebaaly H., Varma S., Maina J.W., Optimizing asphalt mix design process using artificial neural network and genetic algorithm, Construction and Building Materials, 168, pp. 660-670, (2018)
  • [5] Kassem E., Liu W., Scullion T., Et al., Development of compaction monitoring system for asphalt pavements, Construction and Building Materials, 96, pp. 334-345, (2015)
  • [6] Liu D.-H., Wu Y., Continuous measuring and real-time visualization monitoring of pavement lift thickness in highway construction, China Journal of Highway and Transport, 30, 11, pp. 163-169, (2017)
  • [7] Shangguan P., Al-Qadi I., Coenen A., Et al., Algorithm development for the application of ground-penetrating radar on asphalt pavement compaction monitoring, International Journal of Pavement Engineering, 17, 3, pp. 189-200, (2014)
  • [8] Zhu X.-Y., Bai S.-J., Xue G.-P., Et al., Assessment of compaction quality of multi-layer pavement structure based on intelligent compaction technology, Construction and Building Materials, 161, pp. 316-329, (2018)
  • [9] Cha Y.J., Choi W., Buyukozturk O., Deep learning-based crack damage detection using convolutional neural networks, Computer-Aided Civil and Infrastructure Engineering, 32, 5, pp. 361-378, (2017)
  • [10] Sha A.-M., Tong Z., Gao J., Recognition and measurement of pavement disasters based on convolutional neural networks, China Journal of Highway and Transport, 31, 1, pp. 1-10, (2018)