| Citation: |
LI Qingquan, CHEN Ruizhe, TU Wei, CHEN Zhipeng, ZHANG Bochen, YAN Aiguo, YIN Pengcheng. Real-Time Vision-Based Deformation Measurement of Long-Span Bridge with Inertial Sensors[J]. Geomatics and Information Science of Wuhan University, 2023, 48(11): 1834-1843. DOI: 10.13203/j.whugis20230006
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The deformation measurement of long-span bridge alignment is of great significance to the assessment of bridge health and safety. The traditional deformation measurement methods of long-span bridge alignment, such as sensor and close range vision measurement, have shortcomings such as high measurement cost and limited measurement frequency, which are difficult to meet the requirements of high-precision measurement of deformation of long-span bridge alignment.
Combined with dynamic precision engineering measurement and machine vision technology, this paper proposes a method for measuring the deformation of long-span bridge alignment based on inertial cameras. The camera is used to take high-frequency images of bridge measurement targets. At the same time, the inertial sensors are used to correct the measurement errors caused by camera movement. The deformation of long-span bridge alignment is calculated through inertial cameras in series mode, and the corresponding measuring equipment is developed and applied to the health monitoring system of a super large bridge.
The precision experiment results show that root mean square error of the proposed method is 0.38 mm. In engineering applications, the deformation of bridge alignment trend measured by the inertial camera is highly consistent with the static leveling results. The correlation coefficient is better than 99.90%, and the mutual difference is 4.94 mm.
The proposed method can measure the linear deformation of large span bridges with high accuracy and real-time, and has good engineering value.
| [1] |
曾德礼. 大跨度桥梁承载能力鉴定研究[J]. 桥梁建设, 2018, 48(5): 43-47. https://www.cnki.com.cn/Article/CJFDTOTAL-QLJS201805009.htm
Zeng Deli. Study on Identification of Load Bearing Capacity of Long Span Bridge[J]. Bridge Construction, 2018, 48(5): 43-47. https://www.cnki.com.cn/Article/CJFDTOTAL-QLJS201805009.htm
|
| [2] |
李锦华, 张焕涛, 陈水生, 等. 基于移动荷载傅里叶谱的桥梁共振频域分析[J]. 铁道学报, 2022, 44(7): 163-170. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202207019.htm
Li Jinhua, Zhang Huantao, Chen Shuisheng, et al. Frequency-domain Analysis of Bridge Resonance Based on Moving Load Fourier Spectrum[J]. Journal of the China Railway Society, 2022, 44(7): 163-170. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202207019.htm
|
| [3] |
Zhou Y, Xia Y, Fu Y. Analytical Formulas of Beam Deflection Due to Vertical Temperature Difference[J]. Engineering Structures, 2021, 240: 112366. doi: 10.1016/j.engstruct.2021.112366
|
| [4] |
施洲, 蒲黔辉, 岳青. 基于健康监测的高铁大型桥梁运营性能评定[J]. 铁道工程学报, 2017, 34(1): 67-74. doi: 10.3969/j.issn.1006-2106.2017.01.013
Shi Zhou, Pu Qianhui, Yue Qing. Service Performance Evaluation of High Speed Railway Long Span Bridge Based on Healthy Monitoring[J]. Journal of Railway Engineering Society, 2017, 34(1): 67-74. doi: 10.3969/j.issn.1006-2106.2017.01.013
|
| [5] |
魏周春. 高速铁路大跨度桥梁变形对线路平顺度的影响分析[J]. 铁道建筑, 2022, 62(7): 81-85. https://www.cnki.com.cn/Article/CJFDTOTAL-TDJZ202207019.htm
Wei Zhouchun. Analysis on Influence of Long-span High Speed Railway Bridge Deformation on Line Smoothness[J]. Railway Engineering, 2022, 62(7): 81-85. https://www.cnki.com.cn/Article/CJFDTOTAL-TDJZ202207019.htm
|
| [6] |
陈克坚, 杨国静, 胡玉珠. 铁路大跨度混凝土拱桥徐变变形控制因素研究[J]. 铁道工程学报, 2019, 36(4): 48-53. https://www.cnki.com.cn/Article/CJFDTOTAL-TDGC201904010.htm
Chen Kejian, Yang Guojing, Hu Yuzhu. Research on the Creep Control Factors of Railway Long-span Concrete Arch Bridge[J]. Journal of Railway Engineering Society, 2019, 36(4): 48-53. https://www.cnki.com.cn/Article/CJFDTOTAL-TDGC201904010.htm
|
| [7] |
王应良, 陈星宇, 杨国静. 基于运营纵坡控制的大跨铁路和公铁合建桥竖向刚度设计方法[J]. 桥梁建设, 2021, 51(4): 25-30. https://www.cnki.com.cn/Article/CJFDTOTAL-QLJS202104004.htm
Wang Yingliang, Chen Xingyu, Yang Guojing. Vertical Stiffness Design Method Based on Longitudinal Gradient Control in Service Period for Long-Span Rail-cum-Road Bridge[J]. Bridge Construction, 2021, 51(4): 25-30. https://www.cnki.com.cn/Article/CJFDTOTAL-QLJS202104004.htm
|
| [8] |
Ni F T, Zhang J, Noori M N. Deep Learning for Data Anomaly Detection and Data Compression of a Long-Span Suspension Bridge[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(7): 685-700. doi: 10.1111/mice.12528
|
| [9] |
陈睿哲, 涂伟, 李清泉, 等. 融合多源测量数据的桥梁挠度异常探测方法[J]. 测绘通报, 2022(9): 105-110. https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB202209019.htm
Chen Ruizhe, Tu Wei, Li Qingquan, et al. The Method of Bridge Deflection Outlier Detection by Fusing Multi-sourced Surveying Data[J]. Bulletin of Surveying and Mapping, 2022(9): 105-110. https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB202209019.htm
|
| [10] |
Abu Dabous S, Feroz S. Condition Monitoring of Bridges with Non-contact Testing Technologies[J]. Automation in Construction, 2020, 116: 103224. doi: 10.1016/j.autcon.2020.103224
|
| [11] |
Kim M K, Wang Q, Li H. Non-contact Sensing Based Geometric Quality Assessment of Buildings and Civil Structures: A Review[J]. Automation in Construction, 2019, 100: 163-179. doi: 10.1016/j.autcon.2019.01.002
|
| [12] |
Rashidi M, Mohammadi M, Kivi S S, et al. A Decade of Modern Bridge Monitoring Using Terrestrial Laser Scanning: Review and Future Directions[J]. Remote Sensing, 2020, 12(22): 3796. doi: 10.3390/rs12223796
|
| [13] |
Kim D, Kwak Y, Sohn H. Accelerated Cable-stayed Bridge Construction Using Terrestrial Laser Scanning[J]. Automation in Construction, 2020, 117: 103269. doi: 10.1016/j.autcon.2020.103269
|
| [14] |
Qin X, Ding X, Liao M, et al. A Bridge-Tailored Multi-temporal DInSAR Approach for Remote Exploration of Deformation Characteristics and Mechanisms of Complexly Structured Bridges[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 156: 27-50. doi: 10.1016/j.isprsjprs.2019.08.003
|
| [15] |
Xiong S T, Wang C S, Qin X Q, et al. Time-series Analysis on Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) Derived Displacements of the Hong Kong-Zhuhai-Macao Bridge (HZMB) from Sentinel-1A Observations [J]. Remote Sensing, 2021, 13(4): 546. doi: 10.3390/rs13040546
|
| [16] |
Xu S Y, Wang J, Shou W C, et al. Computer Vision Techniques in Construction: A Critical Review[J]. Archives of Computational Methods in Engineering, 2021, 28(5): 3383-3397. doi: 10.1007/s11831-020-09504-3
|
| [17] |
龚健雅. 人工智能时代测绘遥感技术的发展机遇与挑战[J]. 武汉大学学报(信息科学版), 2018, 43(12): 1788-1796. doi: 10.13203/j.whugis20180082
Gong Jianya. Chances and Challenges for Development of Surveying and Remote Sensing in the Age of Artificial Intelligence[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1788-1796. doi: 10.13203/j.whugis20180082
|
| [18] |
Spencer B F, Hoskere V, Narazaki Y. Advances in Computer Vision-based Civil Infrastructure Inspection and Monitoring[J]. Engineering, 2019, 5(2): 199-222.
|
| [19] |
李清泉, 朱家松, 李虹, 等. 基于漂流式胶囊机器人的管道快速检测系统[J]. 中国给水排水, 2021, 37(10): 126-132. https://www.cnki.com.cn/Article/CJFDTOTAL-GSPS202110026.htm
Li Qingquan, Zhu Jiasong, Li Hong, et al. Rapid Detection System of Pipeline Based on Floating Capsule Robot[J]. China Water & Wastewater, 2021, 37(10): 126-132. https://www.cnki.com.cn/Article/CJFDTOTAL-GSPS202110026.htm
|
| [20] |
李清泉, 谷宇, 涂伟, 等. 利用管道胶囊进行排水管网协同检测的新方法[J]. 武汉大学学报(信息科学版), 2021, 46(8): 1123-1130. doi: 10.13203/j.whugis20200509
Li Qingquan, Gu Yu, Tu Wei, et al. Collaborative Inspection for the Sewer Pipe Network Using Pipe Capsules[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1123-1130. doi: 10.13203/j.whugis20200509
|
| [21] |
Peng X, Zhong X G, Zhao C, et al. A UAV-based Machine Vision Method for Bridge Crack Recognition and Width Quantification Through Hybrid Feature Learning[J]. Construction and Building Materials, 2021, 299: 123896.
|
| [22] |
Yu Z W, Shen Y G, Shen C K. A Real-time Detection Approach for Bridge Cracks Based on Yolov4-FPM[J]. Automation in Construction, 2021, 122: 103514.
|
| [23] |
邵帅, 周志祥, 邓国军, 等. 基于非接触远程智能感知的桥梁形态监测试验[J]. 中国公路学报, 2019, 32(11): 91-102. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201911009.htm
Shao Shuai, Zhou Zhixiang, Deng Guojun, et al. Experiment of Structural Morphology Monitoring for Bridges Based on Non-contact Remote Intelligent Perception Method[J]. China Journal of Highway and Transport, 2019, 32(11): 91-102. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201911009.htm
|
| [24] |
Shajihan S A V, Hoang T, Mechitov K, et al. Wireless Smart Vision System for Synchronized Displacement Monitoring of Railroad Bridges[J]. Computer-Aided Civil and Infrastructure Engineering, 2022, 37(9): 1070-1088.
|
| [25] |
Pan P, Xing C, Bai J. et al. A Remote Deflection Detection Method for Long-Span Bridges Using Adaptive Mask and High-Resolution Camera[J]. Measurement, 2022, 201: 111774.
|
| [26] |
Luo L X, Feng M Q, Wu Z Y. Robust Vision Sensor for Multi-point Displacement Monitoring of Bridges in the Field[J]. Engineering Structures, 2018, 163: 255-266.
|
| [27] |
Zhuge S, Xu X P, Zhong L J, et al. Noncontact Deflection Measurement for Bridge Through a Multi-UAVs System[J]. Computer-Aided Civil and Infrastructure Engineering, 2022, 37(6): 746-761.
|
| [28] |
Han Y T, Wu G, Feng D M. Vision-based Displacement Measurement Using an Unmanned Aerial Vehicle[J]. Structural Control and Health Monitoring, 2022, 29(10): e3025.
|
| [29] |
涂伟, 李清泉, 高文武, 等. 基于机器视觉的桥梁挠度实时精密测量方法[J]. 测绘地理信息, 2020, 45(6): 80-87. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXG202006018.htm
Tu Wei, Li Qingquan, Gao Wenwu, et al. Monitoring the Dynamic Deflection of Bridges Using Computer Vision[J]. Journal of Geomatics, 2020, 45(6): 80-87. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXG202006018.htm
|
| [30] |
朱前坤, 崔德鹏, 杜永峰. 基于网络摄像机的桥梁挠度非接触识别[J]. 工程力学, 2022, 39(6): 146-155. https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX202206013.htm
Zhu Qiankun, Cui Depeng, Du Yongfeng. Non-contact Identification of Bridge Deflection Based on Network Camera[J]. Engineering Mechanics, 2022, 39(6): 146-155. https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX202206013.htm
|
| [31] |
李清泉, 张德津, 汪驰升, 等. 动态精密工程测量技术及应用[J]. 测绘学报, 2021, 50(9): 1147-1158. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202109003.htm
Li Qingquan, Zhang Dejin, Wang Chisheng, et al. Technology and Applications of Dynamic and Precise Engineering Surveying[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9): 1147-1158. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202109003.htm
|
| [32] |
李清泉, 毛庆洲. 道路/轨道动态精密测量进展[J]. 测绘学报, 2017, 46(10): 1734-1741. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201710055.htm
Li Qingquan, Mao Qingzhou. Progress on Dynamic and Precise Engineering Surveying for Pavement and Track[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1734-1741. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201710055.htm
|
| [33] |
李清泉, 汪驰升, 熊思婷, 等. 从几何计算到特征提取的广义测量数据处理[J]. 武汉大学学报(信息科学版), 2022, 47(11): 1805-1814. doi: 10.13203/j.whugis20220606
Li Qingquan, Wang Chisheng, Xiong Siting, et al. Generalized Surveying Data Processing: From Geometric Parameters Calculation to Feature Information Extraction[J]. Geomatics and Information Science of Wuhan University, 2022, 47(11): 1805-1814. doi: 10.13203/j.whugis20220606
|
| [34] |
李清泉. 动态精密工程测量[M]. 北京: 科学出版社, 2021.
Li Qingquan. Dynamic Precision Engineering Survey[M]. Beijing: Science Press, 2021.
|
| [35] |
Shah A W, Bangash J I, Khan A W, et al. Comparative Analysis of Median Filter and Its Variants for Removal of Impulse Noise from Gray Scale Images[J]. Journal of King Saud University - Computer and Information Sciences, 2022, 34(3): 505-519.
|
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