桥梁工程中非接触位移测量技术研究综述

晏班夫; 欧阳康; 梁才

doi:10.19818/j.cnki.1671-1637.2024.01.003

桥梁工程中非接触位移测量技术研究综述

doi: 10.19818/j.cnki.1671-1637.2024.01.003

1.
广西大学土木建筑工程学院，广西南宁 530004
2.
广西交通设计集团有限公司，广西南宁 530029

基金项目:

国家自然科学基金项目 U23A20662

广西科技计划项目 2022AB23045

详细信息

作者简介:
晏班夫(1972-)，男，湖南冷水江人，广西大学教授，工学博士，从事工程结构智慧测试与评估、UHPC桥梁新结构研究

中图分类号: U446.1
计量
- 文章访问数: 5861
- HTML全文浏览量: 1813
- PDF下载量: 310
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-08
- 刊出日期: 2024-02-25

Review on research of non-contact displacement measurement technologies in bridge engineering

1.
School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, Guangxi, China
2.
Guangxi Communications Design Group Co., Ltd., Nanning 530029, Guangxi, China

Funds:

National Natural Science Foundation of China U23A20662

Science and Technology Plan Project of Guangxi Province 2022AB23045

More Information

Author Bio:
YAN Ban-fu(1972-), male, professor, PhD, yanbanfu@gxu.edu.cn

Article Text (Baidu Translation)

摘要

摘要:
系统总结了视觉测量、微波雷达与激光测振这3种非接触位移测量技术的测量方法、测量原理与工程应用影响因素，阐述了非接触位移测量技术在桥梁工程动位移测量、模态识别、索杆张力测试中的创新成果，探讨了非接触位移测量面临的关键挑战与后续研究发展方向。分析结果表明：非接触位移测量方法能够实时获取桥梁结构动位移、自振频率、模态振型等信息，测试精度受硬件、算法、测量距离和环境条件等多重因素影响；视觉测量技术易低成本实现面内多目标位移的实时测量，但对环境条件变化较为敏感，且一般应设置靶标，适用于短距离长期或远距离短期情形下的位移监测；微波雷达具有抗天气干扰能力强、测量距离远(可达2 km)、成本可接受、全天候全天时工作、可长期监测等特点，一般也需设置角反射器提高径向位移测试精度；激光测振技术具有微米级位移测量精度，且抗电磁干扰能力强，但难以实现远距离多点同步测量，穿透性较差，易受天气影响，且设备昂贵，适用于短期径向位移监测；应针对桥梁结构监测时长、测量距离、环境条件、单(多)点监测需求、测量精度要求等的不同，选用合适的非接触测量方法；未来可通过提高硬件系统性能提升远距离多点同步测试能力，发展各种自适应环境(光照、大气)扰动校正算法提高位移测试精度和可靠性，将视觉测量、微波雷达、激光测振等技术与加速度计、全站仪、GPS等测试手段进行多源信息融合，通过相互校准减少不确定性，提升不同环境条件下的测量稳健性，实现全天候三维测试。
- 桥梁工程 /
- 非接触测量 /
- 结构位移 /
- 视觉测量 /
- 微波雷达 /
- 激光测振 /
- 信息融合 /
- 索杆张力识别
Abstract:
The measurement methods, principles, and influencing factors during engineering applications of three non-contact displacement measurement technologies were systematically summarized, such as the visual measurement, microwave radar, and laser vibration measurement. The innovative achievements of non-contact displacement measurement technologies in the dynamic displacement measurement, modal identification, and cable tension testing in bridge engineering were introduced. Moreover, the key challenges faced by the non-contact displacement measurement and future research directions were discussed. It is found that non-contact displacement measurement methods can obtain real-time dynamic displacement, natural vibration frequency, modal shape, and other information about bridge structures. The measurement accuracy is affected by multiple factors such as the hardware, algorithm, measurement distance, and environmental conditions. The visual measurement technology makes it easy to achieve real-time in-plane displacement measurement of multiple targets at a low cost, but it is sensitive to change in environmental conditions and generally requires target installation. Therefore, it is suitable for short-distance and long-term or long-distance and short-term displacement monitoring. The microwave radar has the characteristics of strong anti-weather interference capability, long measurement distance (up to 2 km), acceptable cost, all-weather all-time operation, and long-term monitoring. Generally, corner reflectors are needed to improve the accuracy of radial displacement measurement. The laser vibration measurement technology has the micrometer-level displacement measurement accuracy and strong resistance to electromagnetic interference. However, it is difficult to achieve long-distance and multi-target synchronous measurements with poor penetration and is easily affected by environmental conditions. In addition, its equipment is expensive, and thus it is suitable for short-term radial displacement monitoring. Suitable non-contact measurement methods should be selected according to different needs of bridge structures for long-term and short-term monitoring, measurement distance, environmental conditions, single (multiple) target monitoring, and measurement accuracy. In the future, the ability to perform long-distance and multi-target synchronous measurements can be enhanced by improving the performance of the hardware system. Various disturbance correction algorithms for adaptive environments (lighting and atmosphere) can be developed to improve the displacement measurement accuracy and reliability. Furthermore, it is necessary to integrate the visual measurement, microwave radars, and laser vibration measurement technologies with testing methods such as accelerometers, total stations, and global position system (GPS) for multi-source information fusion, thus ensuring mutual calibration to reduce uncertainty, improving the measurement robustness under different environmental conditions, and achieving all-weather three-dimensional testing.
- bridge engineering /
- non-contact measurement /
- structural displacement /
- visual measurement /
- microwave radar /
- laser vibration measurement /
- information fusion /
- estimation of cable tension

HTML全文

图 1 视觉位移测量系统

Figure 1. Visual displacement measurement system

下载: 全尺寸图片幻灯片

图 2 FNCC算法子集搜索

Figure 2. Subset search algorithm of FNCC

下载: 全尺寸图片幻灯片

图 3 IC-GN算法流程

Figure 3. Flow of IC-GN algorithm

下载: 全尺寸图片幻灯片

图 4 DIC视觉位移测量方法的工程应用

Figure 4. Engineering applications of DIC visual displacement measurement method

下载: 全尺寸图片幻灯片

图 5 微波雷达测量系统

Figure 5. Measurement system of microwave radar

下载: 全尺寸图片幻灯片

图 6 CW雷达位移测量

Figure 6. Displacement measurement of CW radar

下载: 全尺寸图片幻灯片

图 7 线性调频连续微波信号

Figure 7. Linear frequency modulation continuous wave signal

下载: 全尺寸图片幻灯片

图 8 频率与时间关系

Figure 8. Frequency-time relationship

下载: 全尺寸图片幻灯片

图 9 多目标测量频率与时间关系

Figure 9. Frequency-time relationship of multi-target measurements

下载: 全尺寸图片幻灯片

图 10 微波雷达位移测量方法工程应用

Figure 10. Engineering applications of microwave radar displacement measurement method

下载: 全尺寸图片幻灯片

图 11 激光测振位移测量系统

Figure 11. Laser vibration displacement measurement system

下载: 全尺寸图片幻灯片

图 12 激光测振位移测量方法的试验研究与工程应用

Figure 12. Experimental research and engineering applications of laser vibration displacement measurement method

下载: 全尺寸图片幻灯片

表 1 非接触位移测量方法对比

Table 1. Comparison of non-contact displacement measurement methods

测量特征与产品	DIC视觉位移测量方法	微波雷达位移测量方法	激光测振位移测量方法
测量精度	面内测距精度较高，面外较低	面内测距精度低，面外较高	面内测距精度低，面外很高
测量距离	远距离大视场与精度矛盾，一般小于500 m，与环境条件有关	可实现大范围远距离测量，可大于1 000 m	远距离一般不超过300 m
测量形式	可多点、全局测量	可多点测量	可多点测量，一般需要多台设备
恶劣天气影响	易受影响	影响小	易受影响
光照影响	易受影响	不受影响	不受影响
电磁干扰影响	影响小	易受影响	不受影响
采样频率	较高，满足土木工程测量要求	高，与测点数有关	高
算法难度与技术成熟度	算法难度较大，技术相对成熟	算法难度大，技术仍在发展	算法难度大，技术仍在发展
现场操作难度	简单	需要一定的技能，较难	需要一定的技能，较难
运维难度	最为方便	一般	难
长期监测适合度	环境条件稳定情形下适合	适合	一般
应用成本	最低	较高	非常高
国内外代表性产品	1.型号：IS103-DMS 厂商：英国Imetrum公司工作距离：可超1 000 m 精度：0.01 mm 2.型号：HPQN-X免靶标多点桥梁挠度仪厂商：北京浩普中兴科技有限公司工作距离：0.1~1 000.0 m 精度：±0.01 mm(检测距离10 m); ±0.1 mm(检测距离100 m); ±1 mm(检测距离1 000 m)	1.型号：IBIS-S 厂商：意大利IDS公司工作距离：最大2 000 m 距离分辨率：0.5 m 精度：0.01 mm 2.型号：GH-DMR-1D 厂商：湖南吉赫信息科技有限公司工作距离：大于500 m 距离分辨率：0.5 m 精度：优于0.1 mm	1.型号：RSV-150 厂商：德国Polytec公司类型：远距离激光测振仪工作距离：大于300 m 位移分辨率：0.3 nm 2. 型号：LV-RFS01 厂商：中国舜宇光学科技有限公司类型：远距离单点激光测振仪工作距离：5~300 m 位移分辨率：优于1 pm
实际工程应用效果	可实现无需靶标的位移测量，但在表面缺乏明显纹理或特征的结构上，实现位移测量较为困难，通常需要在测量点设置靶标；此技术易受环境因素影响，较难实现长期稳定的位移监测和测量；一般应用于环境稳定和表面特征明显的近距离测量，成本最低。	一般情况下需要设置反射靶标才能实现较为精确的径向位移测量，设备价格工程上可接受；在远距离、恶劣环境以及光照条件不佳的情况下，该技术的应用效果较为突出，可实现长期稳定的位移监测和测量。	具有非接触、较远距离、高精度、高空间及速度分辨率的突出优点，但设备价格非常昂贵；对环境变化较为敏感，影响精度；该技术可获得近距结构的模态参数，对于大跨结构，实现低成本模态振型测试仍有一定困难。

下载: 导出CSV

参考文献(153)

[1]	焦峪波. 不确定条件下桥梁结构损伤识别及状态评估的模糊计算方法研究[D]. 长春: 吉林大学, 2012. JIAO Yu-bo. The fuzzy calculation method for damage identification and condition evaluation of bridge under uncertainties[D]. Changchun: Jilin University, 2012. (in Chinese)
[2]	SUTTON M A. Computer vision-based, noncontacting deformation measurements in mechanics: a generational transformation[J]. Applied Mechanics Reviews, 2013, 65(5): 050802. doi: 10.1115/1.4024984
[3]	FENG Dong-ming, FENG M Q. Computer vision for SHM of civil infrastructure: from dynamic response measurement to damage detection—a review[J]. Engineering Structures, 2018, 156: 105-117. doi: 10.1016/j.engstruct.2017.11.018
[4]	WENG Jing-hang, CHEN Lin, SUN Li-min, et al. Fully automated and non-contact force identification of bridge cables using microwave remote sensing[J]. Measurement, 2023, 209: 112508. doi: 10.1016/j.measurement.2023.112508
[5]	熊玉勇, 李松旭, 吴高阳, 等. 基于毫米波感知的形变及振动多点同步测量理论与方法[J]. 中国科学: 技术科学, 2021, 51(9): 998-1010. http://www.cnki.com.cn/Article/CJFDTOTAL-JEXK202109002.htm XIONG Yu-yong, LI Song-xu, WU Gao-yang, et al. Theory and method of multi-point synchronous deformation and vibration measurement based on millimeter-wave sensing[J]. Scientia Sinica Technologica, 2021, 51(9): 998-1010. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-JEXK202109002.htm
[6]	NASSIF H H, GINDY M, DAVIS J. Comparison of laserdoppler vibrometer with contact sensors for monitoring bridge deflection and vibration[J]. NDT and E International, 2005, 38(3): 213-218. doi: 10.1016/j.ndteint.2004.06.012
[7]	董衡. 差分干涉雷达形变测量关键技术研究[D]. 长沙: 国防科学技术大学, 2016. DONG Heng. Research on key techniques of deformation measurement based on radar differential interferometry[D]. Changsha: National University of Defense Technology, 2016. (in Chinese)
[8]	SUTTON M A, WOLTERS W J, PETERS W H, et al. Determination of displacements using an improved digital correlation method[J]. Image and Vision Computing, 1983, 1(3): 133-139. doi: 10.1016/0262-8856(83)90064-1
[9]	邵新星, 陈振宁, 戴云彤, 等. 数字图像相关方法若干关键问题研究进展[J]. 实验力学, 2017, 32(3): 305-325. http://www.cnki.com.cn/Article/CJFDTOTAL-SYLX201703002.htm SHAO Xin-xing, CHEN Zhen-ning, DAI Yun-tong, et al. Research progress of several key problems in digital image correlation method[J]. Journal of Experimental Mechanics, 2017, 32(3): 305-325. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-SYLX201703002.htm
[10]	LEWIS J P. Fast template matching[J]. Vision Interface, 1995, 10: 120-123.
[11]	BRUCK H A, MCNEILL S R, SUTTON M A, et al. Digital image correlation using Newton-Raphson method of partial differential correction[J]. Experimental Mechanics, 1989, 29(3): 261-267. doi: 10.1007/BF02321405
[12]	PAN B, LI K, TONG W. Fast, robust and accurate digital image correlation calculation without redundant computations[J]. Experimental Mechanics, 2013, 53(7): 1277-1289. doi: 10.1007/s11340-013-9717-6
[13]	李得睿. 基于数字图像相关与视频运动放大技术的结构形变测试[D]. 长沙: 湖南大学, 2020. LI De-rui. Structural deformation test based on digital image correlation and video motion magnification[D]. Changsha: Hunan University, 2020. (in Chinese)
[14]	REDDY B S, CHATTERJI B N. An FFT-based technique for translation, rotation, and scale-invariant image registration[J]. IEEE Transactions on Image Processing, 1996, 5(8): 1266-1271. doi: 10.1109/83.506761
[15]	PAN Bing, WANG Yue-jiao, TIAN Long. Automated initial guess in digital image correlation aided by Fourier-Mellin transform[J]. Optical Engineering, 2017, 56(1): 014103. doi: 10.1117/1.OE.56.1.014103
[16]	晏班夫, 李得睿, 徐观亚, 等. 基于快速DIC与正则化平滑技术的结构形变测试[J]. 中国公路学报, 2020, 33(9): 193-205. doi: 10.3969/j.issn.1001-7372.2020.09.019 YAN Ban-fu, LI De-rui, XU Guan-ya, et al. Structural deformation testbased on fast digital image correlation and regularization smoothing techniques[J]. China Journal of Highway and Transport, 2020, 33(9): 193-205. (in Chinese) doi: 10.3969/j.issn.1001-7372.2020.09.019
[17]	潘兵, 谢惠民, 戴福隆. 数字图像相关中亚像素位移测量算法的研究[J]. 力学学报, 2007, 39(2): 245-252. http://www.cnki.com.cn/Article/CJFDTOTAL-LXXB200702014.htm PAN Bing, XIE Hui-min, DAI Fu-long. An investigation of sub-pixel displacements registration algorithms in digital image correlation[J]. Chinese Journal of Theoretical and Applied Mechanics, 2007, 39(2): 245-252. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-LXXB200702014.htm
[18]	BAKER S, MATTHEWS I. Lucas-kanade 20 years on: a unifying framework[J]. International Journal of Computer Vision, 2004, 56: 221-255. doi: 10.1023/B:VISI.0000011205.11775.fd
[19]	PAN Bing, QIAN Ke-mao, XIE Hui-min, et al. Two- dimensional digital image correlation for in-plane displacement and strain measurement: a review[J]. Measurement Scienceand Technology, 2009, 20(6): 062001. doi: 10.1088/0957-0233/20/6/062001
[20]	潘兵, 谢惠民. 数字图像相关中基于位移场局部最小二乘拟合的全场应变测量[J]. 光学学报, 2007, 27(11): 1980-1986. doi: 10.3321/j.issn:0253-2239.2007.11.012 PAN Bing, XIE Hui-min. Full-field strain measurement based on least-square fitting of local displacement for digital image correlation method[J]. Acta Optica Sinica, 2007, 27(11): 1980-1986. doi: 10.3321/j.issn:0253-2239.2007.11.012
[21]	YONEYAMA S, UEDA H. Bridge deflection measurement using digital image correlation with camera movement correction[J]. Materials Transactions, 2012, 53(2): 285-290. doi: 10.2320/matertrans.I-M2011843
[22]	LUO Long-xi, FENG M Q. Vision based displacement sensor with heat haze filtering capability[C]//IWSHM. International Workshop of Structural Health Monitoring. New York: IWSHM, 2017: 3255-3262.
[23]	ZHOU H F, LU L J, LI Z Y, et al. Performance of videogrammetric displacement monitoring technique under varying ambient temperature[J]. Advances in Structural Engineering, 2019, 22(16): 3371-3384. doi: 10.1177/1369433218822089
[24]	刘新艳, 马杰, 张小美, 等. 联合矩阵F范数的低秩图像去噪[J]. 中国图象图形学报, 2014, 19(4): 502-511. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201404002.htm LIU Xin-yan, MA Jie, ZHANG Xiao-mei, et al. Image denoising of low-rank matrix recovery via joint Frobenius norm[J]. Journal of Image and Graphics, 2014, 19(4): 502-511. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201404002.htm
[25]	VALSESIA D, FRACASTORO G, MAGLI E. Deep graph-convolutional image denoising[J]. IEEE Transactions on Image Processing, 2020, 29: 8226-8237. doi: 10.1109/TIP.2020.3013166
[26]	柴家贺, 董明利, 孙鹏, 等. 工业相机自热引起像点漂移模型与补偿方法[J]. 红外与激光工程, 2021, 50(6): 240-250. http://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202106027.htm CHAI Jia-he, DONG Ming-li, SUN Peng, et al. Model and compensation method of image point drift caused by self-heating of industrial camera[J]. Infrared and Laser Engineering, 2021, 50(6): 240-250. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202106027.htm
[27]	刘阳, 崔希民, 范生宏, 等. 相机自热引起像点漂移的实验分析与温度补偿模型研究[J]. 光学技术, 2022, 48(1): 14-20. http://www.cnki.com.cn/Article/CJFDTOTAL-GXJS202201003.htm LIU Yang, CUI Xi-min, FAN Sheng-hong, et al. Experimental analysis and temperature compensation model of camera drift caused by self-heating[J]. Optical Technique, 2022, 48(1): 14-20. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-GXJS202201003.htm
[28]	VON GIOI R G, MONASSE P, MOREL J M, et al. Lens distortion correction with a calibration harp[C]//IEEE. International Conference on Image Processing. New York: IEEE, 2011: 617-620.
[29]	杨守瑞, 段婉莹, 艾文宇, 等. 光场相机建模与畸变校正改进方法[J]. 红外与激光工程, 2023, 52(1): 239-247. http://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202301026.htm YANG Shou-rui, DUAN Wan-ying, AI Wen-yu, et al. Light field camera modeling and distortion correction improvement method[J]. Infrared and Laser Engineering, 2023, 52(1): 239-247. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202301026.htm
[30]	刘纯红, 熊丹枫, 董勇, 等. 图像尺寸测量中镜头畸变的影响及校正研究[J]. 中国检验检测, 2021, 29(2): 10-13. http://www.cnki.com.cn/Article/CJFDTOTAL-XDJL202102003.htm LIU Chun-hong, XIONG Dan-feng, DONG Yong, et al. Research on the influence and calibration of lens distortion in image dimension measurement[J]. China Inspection Body and Laboratory, 2021, 29(2): 10-13. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-XDJL202102003.htm
[31]	王琛影, 何小元. 相关识别中的曲面拟合法[J]. 实验力学, 2000, 15(3): 280-285. doi: 10.3969/j.issn.1001-4888.2000.03.005 WANG Chen-ying, HE Xiao-yuan. Curved surface approximation in correlation recognition method[J]. Journal of Experimental Mechanics, 2000, 15(3): 280-285. (in Chinese) doi: 10.3969/j.issn.1001-4888.2000.03.005
[32]	陈登旭, 刘吉, 武锦辉, 等. 基于改进人工鱼群算法的DIC形变分析[J]. 中国测试, 2020, 46(5): 114-119. http://www.cnki.com.cn/Article/CJFDTOTAL-SYCS202005019.htm CHEN Deng-xu, LIU Ji, WU Jin-hui, et al. DIC deformation analysis based on improved artificial fish swarm algorithm[J]. China amd Measurement Test, 2020, 46(5): 114-119. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-SYCS202005019.htm
[33]	SCHREIER H W, BRAASCH J R, SUTTON M A. Systematic errors in digital image correlation caused by intensity interpolation[J]. Optical Engineering, 2000, 39(11): 2915-2921. doi: 10.1117/1.1314593
[34]	PAN Bing. Bias error reduction of digital image correlation using Gaussian pre-filtering[J]. Optics and Lasers in Engineering, 2013, 51(10): 1161-1167. doi: 10.1016/j.optlaseng.2013.04.009
[35]	徐飞鸿, 代坤. 一种改进的数字图像亚像素位移测量算法[J]. 长沙理工大学学报(自然科学版), 2014, 11(1): 75-80. doi: 10.3969/j.issn.1672-9331.2014.01.012 XU Fei-hong, DAI Kun. An improved algorithm of digital image subpixel displacement measurement[J]. Journal of Changsha University of Science and Technology (Natural Science), 2014, 11(1): 75-80. (in Chinese) doi: 10.3969/j.issn.1672-9331.2014.01.012
[36]	CUI Heng-rui, ZENG Zhou-mo, ZHANG Hui, et al. Reducing the systematic error of DIC using gradient filtering[J]. Measurement, 2023, 207: 112366. doi: 10.1016/j.measurement.2022.112366
[37]	邵新星. 高精度、实时数字图像相关变形测量[D]. 南京: 东南大学, 2018. SHAO Xin-xing. High-accuracy, real-time digital image correlation for deformation measurement[D]. Nanjing: Southeast University, 2018. (in Chinese)
[38]	BROWNJOHN J M W, XU Yan, HESTER D. Vision-based bridge deformation monitoring[J]. Frontiers in Built Environment, 2017, 3: 23.
[39]	LEE J, LEE K C, JEONG S, et al. Long-term displacement measurement of full-scale bridges using camera ego-motion compensation[J]. Mechanical Systems and Signal Processing, 2020, 140: 106651. doi: 10.1016/j.ymssp.2020.106651
[40]	LUO Long-xi, 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. doi: 10.1016/j.engstruct.2018.02.014
[41]	MA Zhan-xiong, CHOI J, SOHN H. Noncontact cable tension force estimation using an integrated vision and inertial measurement system[J]. Measurement, 2022, 199: 111532. doi: 10.1016/j.measurement.2022.111532
[42]	逄鹏. 振动对成像质量的影响及恢复研究[J]. 山东工业技术, 2017(11): 299. http://www.cnki.com.cn/Article/CJFDTOTAL-SDGJ201711269.htm PANG Peng. Study on the influence of vibration on imaging quality and its recovery[J]. Journal of Shandong Industrial Technology, 2017(11): 299. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-SDGJ201711269.htm
[43]	YU Qi-feng, CHAO Zhi-chao, JIANG Guang-wen, et al. The effects of temperature variation on videometric measurement and a compensation method[J]. Image and Vision Computing, 2014, 32(12): 1021-1029. doi: 10.1016/j.imavis.2014.08.011
[44]	ZHOU H F, ZHENG J F, XIE Z L, et al. Temperature effects on vision measurement system in long-term continuous monitoring of displacement[J]. Renewable Energy, 2017, 114: 968-983. doi: 10.1016/j.renene.2017.07.104
[45]	LUO Long-xi, FENG M Q, WU Jian-ping, et al. Modeling and detection of heat haze in computer vision based displacement measurement[J]. Measurement, 2021, 182: 109772. doi: 10.1016/j.measurement.2021.109772
[46]	王迎朝, 祁俊峰, 邵珩, 等. 高温数字图像相关方法关键技术研究[J]. 新技术新工艺, 2022(5): 45-50. http://www.cnki.com.cn/Article/CJFDTOTAL-XJXG202205009.htm WANG Ying-zhao, QI Jun-feng, SHAO Heng, et al. Research on key technologies of methods of high temperature digital image correlation[J]. New Technology and New Process, 2022(5): 45-50. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-XJXG202205009.htm
[47]	YE X W, DONG C Z, LIU T. Environmental effect on vision- based structural dynamic displacement monitoring[C]//PLSE. In Proceedings of the Second International Conference on Performance-based and Life-Cycle Structural Engineering. Brisbane: PLSE, 2015: 261-265.
[48]	DONG C Z, YE X W, LIU T. Non-contact structural vibration monitoring under varying environmental conditions[J]. Vibroengineering Procedia, 2015, 5: 217-222.
[49]	XU Y, ZHANG J, BROWNJOHN J. An accurate and distraction-free vision-based structural displacement measurement method integrating siamese network based tracker and correlation-based template matching[J]. Measurement, 2021, 179: 109506. doi: 10.1016/j.measurement.2021.109506
[50]	DONG Chuan-zhi, CELIK O, CATBAS F N, et al. A robust vision-based method for displacement measurement under adverse environmental factors using spatio-temporal context learning and taylor approximation[J]. Sensors, 2019, 19(14): 3197. doi: 10.3390/s19143197
[51]	ZHANG Kai-hua, ZHANG Lei, YANG M H, et al. Fast tracking via spatio-temporal context learning[J]. arXiv, 2013, DOI: 1311.1939.
[52]	CHAN S H, VÕ D T, NGUYEN T Q. Subpixel motion estimation without interpolation[C]//IEEE. 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. New York: IEEE, 2010: 722-725.
[53]	STEPHEN G A, BROWNJOHN J M W, TAYLOR C A. Measurements of static and dynamic displacement from visual monitoring of the Humber Bridge[J]. Engineering Structures, 1993, 15(3): 197-208. doi: 10.1016/0141-0296(93)90054-8
[54]	JÁUREGUI D V, WHITE K R, WOODWARD C B, et al. Noncontact photogrammetric measurement of vertical bridge deflection[J]. Journal of Bridge Engineering, 2003, 8(4): 212-222. doi: 10.1061/(ASCE)1084-0702(2003)8:4(212)
[55]	WAHBEH A M, CAFFREY J P, MASRI S F. A vision-based approach for the direct measurement of displacements in vibrating systems[J]. Smart Materials and Structures, 2003, 12(5): 785. doi: 10.1088/0964-1726/12/5/016
[56]	SONG Qing-song, WU Jin-rui, WANG Hao-lin, et al. Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method[J]. Mechanical Systems and Signal Processing, 2022, 170: 108822. doi: 10.1016/j.ymssp.2022.108822
[57]	FENG M Q, FUKUDA Y, FENG Dong-ming, et al. Nontarget vision sensor for remote measurement of bridge dynamic response[J]. Journal of Bridge Engineering, 2015, 20(12): 04015023. doi: 10.1061/(ASCE)BE.1943-5592.0000747
[58]	TIAN Long, PAN Bing. Remote bridge deflection measurement using an advanced video deflectometer and actively illuminated LED targets[J]. Sensors, 2016, 16(9): 1344. doi: 10.3390/s16091344
[59]	FENG Dong-ming, FENG M Q. Experimental validation of cost-effective vision-based structural health monitoring[J]. Mechanical Systems and Signal Processing, 2017, 88: 199-211. doi: 10.1016/j.ymssp.2016.11.021
[60]	WANG Shuo. Development of monocular video deflectometer based on inclination sensors[J]. Smart Structures and Systems, An International Journal, 2019, 24(5): 607-616.
[61]	CHEN Gong-fa, LIANG Qiang, ZHONG Wen-tao, et al. Homography-based measurement of bridge vibration using UAV and DIC method[J]. Measurement, 2021, 170: 108683. doi: 10.1016/j.measurement.2020.108683
[62]	丁彤, 田垄. 数字图像相关方法辅助的视频运动放大改进方法及其在位移测量中的应用[J]. 实验力学, 2023, 38(5): 625-633. http://www.cnki.com.cn/Article/CJFDTOTAL-SYLX202305009.htm DING Tong, TIAN Long. An improved video motion magnification method assisted by the digital image correlation method and its application in displacement measurement[J]. Journal of Experimental Mechanics, 2023, 38(5): 625-633. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-SYLX202305009.htm
[63]	唐亮, 吴桐, 刘一军, 等. 融合加速度的桥梁位移响应倾斜摄影监测方法[J]. 仪器仪表学报, 2022, 43(10): 152-164. http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB202210017.htm TANG Liang, WU Tong, LIU Yi-jun, et al. A bridge displacement monitoring method by fusing acceleration and tilt photogrammetry-based measurement[J]. Chinese Journal of Scientific Instrument, 2022, 43(10): 152-164. http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB202210017.htm
[64]	周云, 程依婷. 基于数字图像相关理论的非接触式结构位移测量方法[J]. 湖南大学学报(自然科学版), 2021, 48(5): 1-9. http://www.cnki.com.cn/Article/CJFDTOTAL-HNDX202105001.htm ZHOU Yun, CHENG Yi-ting. Non-contact structural displacement measurement based on digital image correlation method[J]. Journal of Hunan University (Natural Sciences), 2021, 48(5): 1-9. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-HNDX202105001.htm
[65]	TIAN Long, ZHAO Jian-hui, PAN Bing, et al. Full-field bridge deflection monitoring with off-axis digital image correlation[J]. Sensors, 2021, 21(15): 5058. doi: 10.3390/s21155058
[66]	尚洋, 于起峰, 关棒磊, 等. 大型结构变形监测摄像测量研究进展[J]. 实验力学, 2017, 32(5): 593-600. http://www.cnki.com.cn/Article/CJFDTOTAL-SYLX201705001.htm SHANG Yang, YU Qi-feng, GUAN Bang-lei, et al. Recent advances of video metrics for large scale structure deformation monitoring[J]. Journal of Experimental Mechanics, 2017, 32(5): 593-600. http://www.cnki.com.cn/Article/CJFDTOTAL-SYLX201705001.htm
[67]	DONG C Z, YE X W, JIN T. Identification of structural dynamic characteristics based on machine vision technology[J]. Measurement, 2018, 126: 405-416. doi: 10.1016/j.measurement.2017.09.043
[68]	简传熠, 邵帅, 周志祥, 等. 桥梁结构全息模态参数识别方法试验研究[J]. 科学技术与工程, 2020, 20(34): 14257-14264. doi: 10.3969/j.issn.1671-1815.2020.34.045 JIAN Chuan-yi, SHAO Shuai, ZHOU Zhi-xiang, et al. Experimental study on holographic modal parameter identification of bridge structures[J]. Science Technology and Engineering, 2020, 20(34): 14257-14264. (in Chinese) doi: 10.3969/j.issn.1671-1815.2020.34.045
[69]	MERAINANI B, XIONG Bian, BALTAZART V, et al. Experimental investigation of structural modal identification using pixels intensity and motion signals from video-based imaging devices: performance, comparison and analysis[C]//SPIE. Multimodal Sensing and Artificial Intelligence: Technologies and Applications Ⅱ. Washington DC: SPIE, 2021: 31-40.
[70]	CHEN Zhi-wei, RUAN Xu-zhi, ZHANG Yao. Vision-based dynamic response extraction and modal identification of simple structures subject to ambient excitation[J]. Remote Sensing, 2023, 15(4): 962. doi: 10.3390/rs15040962
[71]	CHEN Gong-fa, WU Zhi-hua, GONG Chun-jian, et al. DIC-based operational modal analysis of bridges[J]. Advances in Civil Engineering, 2021, 2021: 6694790.
[72]	YAN Zhao-cheng, TENG Shuai, LUO Wen-jun, et al. Bridge modal parameter identification from UAV measurement based on empirical mode decomposition and fourier transform[J]. Applied Sciences, 2022, 12(17): 8689. doi: 10.3390/app12178689
[73]	FENG Dong-ming, SCARANGELLO T, FENG M Q, et al. Cable tension force estimate using novel noncontact vision-based sensor[J]. Measurement, 2017, 99: 44-52.
[74]	KIM S W, PARK D U, KIM J S, et al. Estimating tension of a prestressed concrete cable-stayed bridge under construction and traffic use conditions using a vision-based system[J]. Structures, 2023, 47: 299-312.
[75]	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. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201911003.htm YE Xiao-wei, DONG Chuan-zhi. Review of computer vision- based structural displacement monitoring[J]. China Journal of Highway and Transport, 2019, 32(11): 21-39. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201911003.htm
[76]	刘辉, 黄欢, 邵帅. 多视域下基于机器视觉的索力测试[J]. 科学技术与工程, 2022, 22(33): 14923-14933. http://www.cnki.com.cn/Article/CJFDTOTAL-KXJS202233045.htm LIU Hui, HUANG Huan, SHAO Shuai. Stay cable force measurement using machine vision in multi-view[J]. Science Technology and Engineering, 2022, 22(33): 14923-14933. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-KXJS202233045.htm
[77]	JO H C, KIM S H, LEE J S, et al. Sag-based cable tension force evaluation of cable-stayed bridges using multiple digital images[J]. Measurement, 2021, 186: 110053.
[78]	YAN Ban-fu, LI De-rui, CHEN Wen-bing, et al. Mode shape-aided cable force determination using digital image correlation[J]. Structural Health Monitoring, 2021, 20(5): 2430-2445.
[79]	WANGCHUK S, SIRINGORINGO D M, FUJINO Y, et al. Vision-based vibration measurement of stay-cables by video motion magnification and dynamic mode decomposition[C]//Springer. Experimental Vibration Analysis for Civil Engineering Structures. Berlin: Springer, 2022: 149-162.
[80]	CHEN Wen-bing, YAN Ban-fu, LIAO Jing-bo, et al. Cable force determination using phase-based video motion magnification and digital image correlation[J]. International Journal of Structural Stability and Dynamics, 2022, 22(7): 2250036.
[81]	CATSAMAS S, SHI Bai-qian, WANG Miao, et al. A low-cost radar-based iot sensor for noncontact measurements of water surface velocity and depth[J]. Sensors, 2023, 23(14): 6314.
[82]	张峻橦, 叶明, 夏伟杰. 基于CW雷达的二维运动轨迹高精度测量方法[J]. 传感器与微系统, 2018, 37(11): 25-27, 30. http://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ201811007.htm ZHANG Jun-tong, YE Ming, XIA Wei-jie. 2D motion track measurement method with high precision based on CW radar[J]. Transducer and Microsystem Technologies, 2018, 37(11): 25-27, 30. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ201811007.htm
[83]	陈伟民, 李存龙. 基于微波雷达的位移/距离测量技术[J]. 电子测量与仪器学报, 2015, 29(9): 1251-1265. http://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201509001.htm CHEN Wei-min, LI Cun-long. Radar-based displacement/ distance measuring techniques[J]. Journal of Electronic Measurement and Instrumentation, 2015, 29(9): 1251-1265. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201509001.htm
[84]	VINCI G, LINDNER S, BARBON F, et al. Six-port radar sensor for remote respiration rate and heartbeat vital-sign monitoring[J]. IEEE Transactions on Microwave Theory and Techniques, 2013, 61(5): 2093-2100.
[85]	GU Chang-zhan, LI Rui-jiang, ZHANG Hua-liang, et al. Accurate respiration measurement using DC-coupled continuous-wave radar sensor for motion-adaptive cancer radiotherapy[J]. IEEE Transactions on Biomedical Engineering, 2012, 59(11): 3117-3123.
[86]	GUAN S Y, RICE J A, LI C Z, et al. Automated DC offset calibration strategy for structural health monitoring based on portable CW radar sensor[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(12): 3111-3118.
[87]	李存龙. 面向大型结构的多普勒/调频连续波雷达无源目标位移测量系统研究[D]. 重庆: 重庆大学, 2015. LI Cun-long. Doppler/LFMCW radar system with passive target for displacement measurement of large infrastructure[D]. Chongqing: Chongqing University, 2015. (in Chinese)
[88]	张超, 朱莉, 林琳. 毫米波/亚毫米波大气传输特性研究[J]. 微波学报, 2015, 31(增2): 14-17. http://www.cnki.com.cn/Article/CJFDTOTAL-WBXB2015S2004.htm ZHANG Chao, ZHU Li, LIN Lin. Research on the atmospheric transmission characteristics of millimeter wave/ submillimeter wave[J]. Journal of Microwaves, 2015, 31(S2): 14-17. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-WBXB2015S2004.htm
[89]	李莎莎. 干扰对FMCW雷达测量精度的影响研究[J]. 交通科技与经济, 2021, 23(2): 63-67. http://www.cnki.com.cn/Article/CJFDTOTAL-KJJJ202102010.htm LI Sha-sha. Research on the influence of interference on measurement accuracy of FMCW radar[J]. Technology and Economy in Areas of Communications, 2021, 23(2): 63-67. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-KJJJ202102010.htm
[90]	RÖDELSPERGER S, LÄUFER G, GERSTENECKER C, et al. Monitoring of displacements with ground-based microwave interferometry: IBIS-S and IBIS-L[J]. Journal of Applied Geodesy, 2010, 4(1): 41-54.
[91]	LEE H, LEE J H, CHO S J, et al. An experiment of GB-SAR interperometric measurement of target displacement and atmospheric correction[C]//IEEE. International Geoscience and Remote Sensing Symposium. New York: IEEE, 2008: 240-243.
[92]	LIU Xiang-lei, TONG Xiao-hua, DING Ke-liang, et al. Measurement of long-term periodic and dynamic deflection of the long-span railway bridge using microwave interferometry[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(9): 4531-4538. doi: 10.1109/JSTARS.2015.2464240
[93]	MICHEL C, KELLER S. Advancing ground-based radar processing for bridge infrastructure monitoring[J]. Sensors, 2021, 21(6): 2172. doi: 10.3390/s21062172
[94]	KIM S, NGUYEN C. A displacement measurement technique using millimeter-wave interferometry[J]. IEEE Transactions on Microwave Theory and Techniques, 2003, 51(6): 1724-1728. doi: 10.1109/TMTT.2003.812575
[95]	PIERACCINI M, LUZI G, MECATTI D, et al. Remote sensing of building structural displacements using a microwave interferometer with imaging capability[J]. NDT and E International, 2004, 37(7): 545-550. doi: 10.1016/j.ndteint.2004.02.004
[96]	PIERACCINI M, FRATINI M, PARRINI F, et al. High-speed CW step-frequency coherent radar for dynamic monitoring of civil engineering structures[J]. Electronics Letters, 2004, 40(14): 907-908. doi: 10.1049/el:20040549
[97]	KIM S, NGUYEN C. On the development of a multifunction millimeter-wave sensor for displacement sensing and low-velocity measurement[J]. IEEE Transactions on Microwave Theory and Techniques, 2004, 52(11): 2503-2512. doi: 10.1109/TMTT.2004.837153
[98]	侯庆文, 陈先中, 王小攀, 等. 改进的FMCW信号加权补偿校正相位差法[J]. 仪器仪表学报, 2010, 31(4): 721-726. http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201004001.htm HOU Qing-wen, CHEN Xian-zhong, WANG Xiao-pan, et al. Improved phase-difference algorithm with weighted compensation and correction for FMCW signal[J]. Chinese Journal of Scientific Instrument, 2010, 31(4): 721-726. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201004001.htm
[99]	MA Zhan-xiong, CHOI J, SOHN H. Continuous bridge displacement estimation using millimeter-wave radar, strain gauge and accelerometer[J]. Mechanical Systems and Signal Processing, 2023, 197: 110408. doi: 10.1016/j.ymssp.2023.110408
[100]	ZHENG Hong-xing. A displacement and velocity measurement technique using millimeter-wave sensor[J]. International Journal of Infrared and Millimeter Waves, 2005, 26(9): 1277-1290. doi: 10.1007/s10762-005-7603-8
[101]	LU L, LI C Z, RICE J A. A software-defined multifunctional radar sensor for linear and reciprocal displacement measurement[C]//IEEE. 2011 IEEE Topical Conference on Wireless Sensors and Sensor Networks. New York: IEEE, 2011: 17-20.
[102]	WANG G C, GU C Z, RICE J F, et al. Highly accurate noncontact water level monitoring using continuous-wave Doppler radar[C]//IEEE. 2013 IEEE Topical Conference on Wireless Sensors and Sensor Networks. New York: IEEE, 2013: 19-21.
[103]	郑大青, 陈伟民, 章鹏, 等. 非调制连续微波雷达发射机泄漏影响研究[J]. 仪器仪表学报, 2014, 35(4): 775-780. http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201404008.htm ZHENG Da-qing, CHEN Wei-min, ZHANG Peng, et al. Research on the effect of transmitter leakage in non-modulated continuous microwave radar[J]. Chinese Journal of Scientific Instrument, 2014, 35(4): 775-780. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201404008.htm
[104]	ZHENG Da-qing, CHEN Wei-min, CHEN Li, et al. A microwave radar system based on carrier modulation and heterodyne phase difference detecting with time-to-digital converter[J]. IEICE Electronics Express, 2014, 11(20): 20140791. doi: 10.1587/elex.11.20140791
[105]	郑大青, 胡顺仁, 李双, 等. 大型建筑的有源微波相位雷达位移测量方法[J]. 仪器仪表学报, 2018, 39(4): 44-52. http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201804006.htm ZHENG Da-qing, HU Shun-ren, LI Shuang, et al. Displacement measurement method using microwave phase radar with active transponder for large buildings[J]. Chinese Journal of Scientific Instrument, 2018, 39(4): 44-52. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201804006.htm
[106]	LUZI G, CROSETTO M, FERNÁNDEZ E. Radar interferometry for monitoring the vibration characteristics of buildings and civil structures: recent case studies in Spain[J]. Sensors, 2017, 17(4): 669. doi: 10.3390/s17040669
[107]	XING Cheng, WANG Peng, DONG Wei. Research on the bridge monitoring method of ground-based radar[J]. Arabian Journal of Geosciences, 2020, 13(23): 1267. doi: 10.1007/s12517-020-06283-w
[108]	黄声享, 罗力, 何超. 地面微波干涉雷达与GPS测定桥梁挠度的对比试验分析[J]. 武汉大学学报(信息科学版), 2012, 37(10): 1173-1176. http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201210008.htm HUANG Sheng-xiang, LUO Li, HE Chao. Comparative test analysis for determining bridge deflection by using ground-based SAR and GPS[J]. Geomatics and Information Science of Wuhan University, 2012, 37(10): 1173-1176. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201210008.htm
[109]	LI Cun-long, CHEN Wei-min, LIU Gang, et al. A noncontact FMCW radar sensor for displacement measurement in structural health monitoring[J]. Sensors, 2015, 15(4): 7412-7433. doi: 10.3390/s150407412
[110]	刘德煜. GPS与微波干涉测量在桥梁动挠度测量中的对比分析[J]. 桥梁建设, 2009(3): 81-84. http://www.cnki.com.cn/Article/CJFDTOTAL-QLJS200903020.htm LIU De-yu. Comparative analysis of GPS measurement and microwave interference measurement applied to measurement of bridge dynamic deflection[J]. Bridge Construction, 2009(3): 81-84. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-QLJS200903020.htm
[111]	GUAN Shan-yue, BRIDGE J A, LI Chang-zhi, et al. Smart radar sensor network for bridge displacement monitoring[J]. Journal of Bridge Engineering, 2019, 24(1): 04018102. doi: 10.1061/(ASCE)BE.1943-5592.0001322
[112]	KURAS P, OWERKO T, ORTYL Ł, et al. Advantages of radar interferometry for assessment of dynamic deformation of bridge[C]//IABMAS. Proceedings of the Sixth International IABMAS Conference. Stresa: IABMAS, 2012: 885-891.
[113]	ZHANG Guang-wei, WU Yi-lin, ZHAO Wen-ju, et al. Radar-based multipoint displacement measurements of a 1200 m-long suspension bridge[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 167: 71-84. doi: 10.1016/j.isprsjprs.2020.06.017
[114]	ZHANG Bo-chen, DING Xiao-li, WERNER C, et al. Dynamic displacement monitoring of long-span bridges with a microwave radar interferometer[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 138: 252-264. doi: 10.1016/j.isprsjprs.2018.02.020
[115]	PINIOTIS G, GIKAS V, MPIMIS T, et al. Deck and cable dynamic testing of a single-span bridge using radar interferometry and videometry measurements[J]. Journal of Applied Geodesy, 2016, 10(1): 87-94.
[116]	李金鹿, 肖春文, 朱尚清. 安装阻尼器拉索索力测试方法研究[J]. 市政技术, 2020, 38(增1): 142-146. http://www.cnki.com.cn/Article/CJFDTOTAL-SZJI2020S1042.htm LI Jin-lu, XIAO Chun-wen, ZHU Shang-qing. Study on cable force test method of cable with damper[J]. Journal of Municipal Technology, 2020, 38(S1): 142-146. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-SZJI2020S1042.htm
[117]	王建, 王翔, 周智敏. 微型桥梁索力测量毫米波雷达[J]. 国防科技大学学报, 2022, 44(2): 118-122. http://www.cnki.com.cn/Article/CJFDTOTAL-GFKJ202202015.htm WANG Jian, WANG Xiang, ZHUO Zhi-min. Nano millimeter wave radar for bridge cable tension measurement[J]. Journal of National University of Defense, 2022, 44(2): 118-122. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-GFKJ202202015.htm
[118]	ZHAO Wen-ju, ZHANG Guang-wei, ZHANG Jian. Cable force estimation of a long-span cable-stayed bridge with microwave interferometric radar[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(12): 1419-1433. doi: 10.1111/mice.12557
[119]	苏永江. 一种激光测量干涉仪的研究[J]. 中国校外教育(理论), 2008(2): 112, 114. doi: 10.3969/j.issn.1004-8502-B.2008.02.098 SU Yong-jiang. Research on a lasermeasuring interferometer[J]. Afterschool Education In China (Theory), 2008(2): 112, 114. (in Chinese) doi: 10.3969/j.issn.1004-8502-B.2008.02.098
[120]	QUENELLE R C. Nonlinearity in interferometer measurements[J]. Hewlett Packard Journal, 1983, 34: 10.
[121]	EOM T B, KIM J Y, JEONG K. The dynamic compensation of nonlinearity in a homodyne laser interferometer[J]. Measurement Science and Technology, 2001, 12(10): 1734-1738. doi: 10.1088/0957-0233/12/10/318
[122]	EOM T B, KIM J A, KANG C S, et al. A simple phase-encoding electronics for reducing the nonlinearity error of a heterodyne interferometer[J]. Measurement Science and Technology, 2008, 19(7): 075302. doi: 10.1088/0957-0233/19/7/075302
[123]	张埔榛, 吴军, 黄庚华. 单频激光干涉测振仪的非线性误差主动补偿法[J]. 激光与光电子学进展, 2018, 55(8): 081204. http://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201808040.htm ZHANG Pu-zhen, WU Jun, HUANG Geng-hua. Active compensation method of nonlinear error in homodyne laser interferometer for vibration measurement[J]. Laser and Optoelectronics Progress, 2018, 55(8): 081204. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201808040.htm
[124]	DAI Gao-liang, POHLENZ F, DANZEBRINK H U, et al. Improving the performance of interferometers in metrological scanning probe microscopes[J]. Measurement Science and Technology, 2004, 5(2): 444.
[125]	WANG Chen, BURNHAM-FAY E D, ELLIS J D. Real-time FPGA-based Kalman filter for constant and non-constant velocity periodic error correction[J]. Precision Engineering, 2017, 48: 133-143. doi: 10.1016/j.precisioneng.2016.11.013
[126]	GUO Ji-hua, ZHANG Yan, SHEN Shuai. Compensation of nonlinearity in a new optical heterodyne interferometer with doubled measurement resolution[J]. Optics Communications, 2000, 184(1/2/3/4): 49-55.
[127]	KEEM T, GONDA S, MISUMI I, et al. Removing nonlinearity of a homodyne interferometer by adjusting the gains of its quadrature detector systems[J]. Applied Optics, 2004, 43(12): 2443-2448. doi: 10.1364/AO.43.002443
[128]	LU Zhen-gang, ZHANG Yun-long, LIANG Yao-ting, et al. Measuring the laser polarization state and PBS transmission coefficients in a heterodyne laser interferometer[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67(3): 706-714. doi: 10.1109/TIM.2017.2786579
[129]	黄磊, 李胤玉, 林高远, 等. 影响激光监听效果的因素研究[J]. 科技风, 2020(35): 81-82. http://www.cnki.com.cn/Article/CJFDTOTAL-KJFT202035040.htm HUANG Lei, LI Yin-yu, LIN Gao-yuan, et al. Study on the factors influencing the effect of laser monitoring[J]. Technology Wind, 2020(35): 81-82. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-KJFT202035040.htm
[130]	LYU Tao, HAN Xi-yu, WU Shi-song, et al. The effect of speckles noise on the laser doppler vibrometry for remote speech detection[J]. Optics Communications, 2019, 440: 117-125. doi: 10.1016/j.optcom.2019.02.014
[131]	马勰, 李明, 朱磊磊, 等. 激光多普勒测振仪误差分析[J]. 电子世界, 2022(2): 84-85. http://www.cnki.com.cn/Article/CJFDTOTAL-ELEW202202037.htm MA Xie, LI Ming, ZHU Lei-lei, et al. Error analysis of laser Doppler vibrometer[J]. Electronics World, 2022(2): 84-85. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-ELEW202202037.htm
[132]	吴世松. 中远程光纤激光多普勒微振动检测技术研究[D]. 长春: 中国科学院大学, 2020. WU Shi-song. Research on remote fiber laser Doppler micro-vibration detection[D]. Changchun: University of Chinese Academy of Sciences, 2020. (in Chinese)
[133]	HALLIWELL N A. The laser torsional vibrometer: a step forward in rotating machinery diagnostics[J]. Journal of Sound and Vibration, 1996, 190(3): 399-418. doi: 10.1006/jsvi.1996.0071
[134]	吕宏诗, 刘彬. 激光多普勒测振技术的最新进展[J]. 激光技术, 2005, 29(2): 176-179. http://www.cnki.com.cn/Article/CJFDTOTAL-JGJS20050200K.htm LYU Hong-shi, LIU Bin. Latest development of laser Doppler technique in vibration measurement[J]. Laser Technology, 2005, 29(2): 176-179. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-JGJS20050200K.htm
[135]	黄浩钧. 激光多普勒干涉的双通道振动测量系统[D]. 杭州: 浙江大学, 2017. HUANG Hao-jun. Dual channels vibration measurement system based on laser Doppler interference[D]. Hangzhou: Zhejiang University, 2017. (in Chinese)
[136]	姚欣. 激光多普勒振动检测技术的研究[D]. 天津: 天津大学, 2004. YAO Xin. Laser Doppler study on vibration detecting technology of laser Doppler[D]. Tianjin: Tianjin University, 2004. (in Chinese)
[137]	王豹亭. 零差激光测振技术的研究[D]. 成都: 电子科技大学, 2011. WANG Bao-ting. Research on zero-difference laser vibration measurement technology[D]. Chengdu: University of Electronic Science and Technology of China, 2011. (in Chinese)
[138]	董振升. 激光多普勒远程测试仪在高速铁路桥梁挠度测试中的应用[J]. 铁道建筑, 2016(7): 42-44. doi: 10.3969/j.issn.1003-1995.2016.07.11 DONG Zhen-sheng. Application of remote laser Doppler tester in measuring high speed railway bridge deflection[J]. Railway Engineering, 2016(7): 42-44. (in Chinese) doi: 10.3969/j.issn.1003-1995.2016.07.11
[139]	CHO S, LEE J, SIM S H. Comparative study on displacement measurement sensors for high-speed railroad bridge[J]. Smart Structures and Systems, 2018, 21(5): 637-652.
[140]	苏永华, 袁磊, 董亮, 等. 铁路桥梁非接触检测技术发展[J]. 铁道建筑, 2022, 62(1): 11-17. http://www.cnki.com.cn/Article/CJFDTOTAL-TDJZ202201003.htm SU Yong-hua, YUAN Lei, DONG Liang, et al. Development of non-contact detection technology for railway bridge[J]. Railway Engineering, 2022, 62(1): 11-17. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-TDJZ202201003.htm
[141]	GARG P, MOREU F, OZDAGLI A, et al. Noncontact dynamic displacement measurement of structures using a moving laser Doppler vibrometer[J]. Journal of Bridge Engineering, 2019, 24(9): 04019089. doi: 10.1061/(ASCE)BE.1943-5592.0001472
[142]	GARG P, NASIMI R, OZDAGLI A, et al. Measuring transverse displacements using unmanned aerial systems laser Doppler vibrometer (UAS-LDV): development and field validation[J]. Sensors, 2020, 20(21): 6051. doi: 10.3390/s20216051
[143]	PEPI C, GIOFFRÈ M, COMANDUCCI G, et al. Dynamic characterization of a severely damaged historic masonry bridge[J]. Procedia Engineering, 2017, 199: 3398-3403. doi: 10.1016/j.proeng.2017.09.579
[144]	ABEDIN M, MEHRABI A B. Bridge damage identification through frequency changes[C]//SPIE. Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems. Washington DC: SPIE, 2021: 21-27.
[145]	ABEDIN M, MEHRABI A B. Health monitoring of steel box girder bridges using non-contact sensors[J]. Structures, 2021, 34: 4012-4024. doi: 10.1016/j.istruc.2021.10.021
[146]	蔡劭佳, 王飞. 应用激光测振技术提高模态试验测量准确度[J]. 宇航计测技术, 2012, 32(6): 13-16. http://www.cnki.com.cn/Article/CJFDTOTAL-YHJJ201206006.htm CAI Shao-jia, WANG Fei. Improving the modal measure accuracy by the laser vibration technique[J]. Journal of Astronautic Metrology and Measurement, 2012, 32(6): 13-16. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-YHJJ201206006.htm
[147]	MIYASHITA T, NAGAI M. Vibration-based structural health monitoring for bridges using laser Doppler vibrometers and MEMS-based technologies[J]. International Journal of Steel Structures, 2008, 8(4): 325-331.
[148]	黄智德, 谢谟文, 杜岩, 等. 激光测振仪在斜拉索索力检测中的应用研究[J]. 公路, 2018, 63(5): 109-113. doi: 10.3969/j.issn.1671-2668.2018.05.030 HUANG Zhi-de, XIE Mo-wen, DU Yan, et al. Application of laser Doppler vibration in cable force detection[J]. Highway, 2018, 63(5): 109-113. (in Chinese) doi: 10.3969/j.issn.1671-2668.2018.05.030
[149]	孙立国, 江守燕, 杜成斌. 非接触检测技术在土木工程中的应用[J]. 工程与试验, 2022, 62(4): 115-119. http://www.cnki.com.cn/Article/CJFDTOTAL-SNSN202204033.htm SUN Li-guo, JIANG Shou-yan, DU Cheng-bin. Applications of non-contact detection technology to civil engineering[J]. Engineering and Test, 2022, 62(4): 115-119. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-SNSN202204033.htm
[150]	MEHRABI A B, FARHANGDOUST S. A laser-based noncontact vibration technique for health monitoring of structural cables: background, success, and new developments[J]. Advances in Acoustics and Vibration, 2018, 2018(1): 8640674.
[151]	WANG Tao, LI Rui, ZHU Zhi-gang, et al. Active stereo vision for improving long range hearing using a laser Doppler vibrometer[C]//IEEE. 2011 IEEE Workshop on Applications of Computer Vision (WACV). New York: IEEE, 2011: 564-569.
[152]	CHO S, SIM S H, KIM E. On-site performance evaluation of a vision-based displacement measurement system[J]. Journal of the Korea Academia-Industrial Cooperation Society, 2014, 15(9): 5854-5860. doi: 10.5762/KAIS.2014.15.9.5854
[153]	ZHANG Zheng-you. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334. doi: 10.1109/34.888718