基于计算机视觉的复杂隔震结构支座变形监测及分析

杜永峰; 李向雄; 张超; 李超; 麦麦提明·图尔迪麦麦提; 马振和

doi:10.13204/j.gyjzG22012407

基于计算机视觉的复杂隔震结构支座变形监测及分析

doi: 10.13204/j.gyjzG22012407

1. 兰州理工大学防震减灾研究所, 兰州 730050;
2. 兰州理工大学土木工程减震隔震技术研发甘肃省国际科技合作基地, 兰州 730050;
3. 中国建筑第八工程局有限公司西北公司, 西安 710001

基金项目:

国家自然科学基金项目（52178291，51778276）。

详细信息

作者简介:
杜永峰,男,1962年出生,博士,教授。

通讯作者:
李向雄,xiangxiong_li@163.com。

计量
- 文章访问数: 71
- HTML全文浏览量: 6
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2022-01-24
- 网络出版日期: 2023-03-22

Monitoring and Analysis for Support Deformation of Complex Seismic Isolation Structures Based on Computer Vision

1. Institute of Earthquake Protection and Disaster Mitigation, Lanzhou University of Technology, Lanzhou 730050, China;
2. International Research Base of Seismic Mitigation and Isolation of Gansu Province, Lanzhou University of Technology, Lanzhou 730050, China;
3. Northwest Branch of China Construction the Eighth Engineering Bureau Co., Ltd., Xi'an 710001, China

摘要

摘要: 对于超长复杂隔震结构体系，隔震支座在施工阶段易发生较大的侧向位移现象，实时、准确地监测隔震支座变形情况是指导施工进度和评判结构实际受力的有效依据。传统的接触式位移测量方法受维度、精度、操作和安装繁琐等限制，而采用基于计算机视觉的非接触测量方法可有效克服传统方法的不足。基于计算机视觉技术，提出一种位移监测系统，以网络摄像头为采集设备，采用基于颜色匹配技术，实现目标位移测量。该系统在西部某大型航站楼进行了应用，用于结构隔震支座位移的监测，同时对隔震支座的变形和温度的监测数据进行分析。结果表明，基于计算机视觉的监测系统能够精确测量隔震支座变形，可作为一个自动、经济的智能系统对结构长期服务。最后，给出在温度变化下超长隔震结构隔震支座的变形规律。
- 计算机视觉 /
- 超长隔震结构 /
- 结构健康监测 /
- 连续自适应均值漂移算法 /
- 隔震支座变形
Abstract: For the ultra-long and complex isolation structures, the isolation supports engendered a large lateral displacement phenomenon easily during construction stages. Real-time and accurate deformation monitoring for isolation supports is an effective basis for guiding construction in progress and judging actual stress of structures. The traditional measurement method of contact displacement is restricted by dimensions, accuracy, complicated operation, and installation, etc. The use of the non-contact measurement method based on computer vision can effectively overcome shortcomings of the traditional method. A displacement monitoring system based on computer vision was proposed. Network cameras were used as acquisition devices, and the color matching technique was used to realize displacement measurement of targets. The system was applied in a large terminal building in the west of China, and used for displacement monitoring of isolation supports in the building, and the monitoring data of deformation and temperature of isolation supports were analyzed. The results showed that the monitoring system based on computer vision could accurately measure deformation of isolation supports, and serve as an automatic and economical intelligent system for the structure for a long time. The deformation law changed with temperature for isolation supports of the ultra-long isolation structure was given.
- computer vision /
- ultra-long seismic isolation structure /
- health monitoring /
- continuous adaptive mean shift algorithm /
- horizontal displacement of seismic isolation support

HTML全文

参考文献(13)

[1]	GUL M, CATBAS F N, HATTORI H. Image-based monitoring of open gears of movable bridges for condition assessment and maintenance decision making[J]. Journal of Computing in Civil Engineering, 2015, 29(2). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000307.
[2]	FAULKNER C, BARTON F W, BABER T T, et al. Determination of bridge response using accleration data[R]. Virginia:Virginia Transportation Research Council, 1996:1-10.
[3]	周颖, 张立迅, 刘彤, 等.基于计算机视觉的结构系统识别[J]. 土木工程学报, 2018, 51(11):21-27.
[4]	CHEN I H, HO S C, SU M B. Computer vision application programming for settlement monitoring in a drainage tunnel[J]. Automation in Construction,2020, 110. http://doi.org/10.1016/j.autcon.2019.103011.
[5]	XU Y, BROWNJOHN J, KONG D. A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge[J]. Structural Control & Health Monitoring, 2018, 25(5):1-23.
[6]	YE X W, DONG C Z, LIU T. Image-based structural dynamic displacement measurement using different multi-object tracking algorithms[J]. Smart Structures and Systems, 2016, 17(6):935-956.
[7]	叶肖伟, 董传智. 基于计算机视觉的结构位移监测综述[J]. 中国公路学报, 2019,32(11):21-39.
[8]	DONG C Z, CATBAS F N. A review of computer vision-based structural health monitoring at local and global levels[J]. Structural Health Monitoring, 2020,20(2). https://doi.org/10.1177/147592172093558.
[9]	HARTLEY R, ZISSERMAN A. Multiple view geometry in computer vision[M]. 2nd ed. Cambridge:Cambridge University Press, 2003.
[10]	FISCHLER M A, BOLLES R C. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6):619-638.
[11]	RIBEIRO D, CALÇADA R, FERREIRA J, et al. Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system[J]. Engineering Structures, 2014, 75(75)164-180.
[12]	BRADSKI G R, LAB M, CLARA S, et al. Computer vision face tracking for use in a perceptual user interface[C]//Fourth IEEE Workshop on Applications of Computer Vision. New York:IEEE, 1998.
[13]	FUKUNAGA K, HOSTETLER L. The estimation of the gradient of a density function, with applications in pattern recognition[J]. Information Theory, IEEE Transactions, 1975. 2209000020800909宋成浩.fbd