Technology and applications of dynamic and precise engineering surveying

doi:10.11947/j.AGCS.2021.20210172

Abstract

Abstract: The dynamic precision engineering survey is not only used in the construction but also in monitoring the dynamic change during usage. With the rapid popularization of various sensors and automated mobile platform, such as intelligent vehicles, unmanned aerial vehicle (UAV), unmanned ship, robots etc., the engineering survey is upgraded in an automated, dynamic and intelligent way. It is now armed with the ability to survey in movement. Surveying devices, including mature surveying robots, mobile surveying vehicle, etc., have been proposed. Another way is to assemble devices delicately for specific applications, which can achieve dynamic and precise measurements of position, posture, internal deform and external shape. Focusing on the intelligent device with integrated sensors and specific applications, this paper puts forward the integration principle of multiple sensors and the advanced data processing methods for intelligent engineering surveying. This paper also demonstrates several applications of these devices, such as internal deformation monitoring of the rockfill dam, damage monitoring of the urban underground water pipelines, surveying of the road damages and the damage detection on the south-to-north water diversion project (SNWDP), to show different requirements and solutions in different applications.

Key words: dynamic precise engineering surveying, intelligent surveying, intelligent surveying devices, road surveying, internal deformation surveying of rockfill dams

CLC Number:

P258

LI Qingquan, ZHANG Dejin, WANG Chisheng, CHEN Zhipeng, TU Wei. Technology and applications of dynamic and precise engineering surveying[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9): 1147-1158.

References

[1] 张正禄. 工程测量学[M]. 2版. 武汉: 武汉大学出版社,2013. ZHANG Zhenglu. Engineering surveying[M]. 2nd ed. Wuhan: Wuhan University Press, 2013.
[2] 宋超智, 陈翰新, 温宗勇. 大国工程测量技术创新与发展[M]. 北京: 中国建筑工业出版社,2019. SONG Chaozhi, CHEN Hanxin, WEN Zongyong. Innovation and development of engineering surveying[M].Beijing: China Architecture Publishing & Media Co., Ltd., 2019.
[3] 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. DOI: 10.11947/j.AGCS.2017.20170323. 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. DOI: 10.11947/j.AGCS.2017.20170323.
[4] 任晓春. 高速铁路精密工程测量技术[M]. 成都: 西南交通大学出版社, 2018. REN Xiaochun. Precise engineering surveying of high-speed railway[M]. Chengdu: Southwest Jiaotong University Press, 2018.
[5] MAO Qingzhou, CUI Hao, HU Qingwu, et al. A rigorous fastener inspection approach for high-speed railway from structured light sensors[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 143(SEP.):249-267.
[6] 李广云, 范百兴. 精密工程测量技术及其发展[J]. 测绘学报, 2017, 46(10): 1742-1751.DOI: 10.11947/j.AGCS.2017.20170313. LI Guangyun, FAN Baixing. The development of precise engineering surveying technology[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1742-1751. DOI: 10.11947/j.AGCS.2017.20170313.
[7] 李清泉. 动态精密工程测量[M]. 北京: 科学出版社,2020. LI Qingquan. Dynamic and precise engineer surveying[M]. Beijing: Science Press,2020.
[8] 李清泉, 朱家松, 汪驰升, 等. 海岸带区域船载水岸一体综合测量技术概述[J]. 测绘地理信息, 2017, 42(5): 1-6. LI Qingquan, ZHU Jiasong, WANG Chisheng, et al. Shipborne combined laser and bathymetric surveying technique in coastal zone: an overview[J]. Journal of Geomatics, 2017, 42(5): 1-6.
[9] 李清泉, 卢艺, 胡水波, 等. 海岸带地理环境遥感监测综述[J]. 遥感学报, 2016, 20(5): 1216-1229. LI Qingquan, LU Yi, HU Shuibo, et al. Review of remotely sensed geo-environmental monitoring of coastal zones[J]. Journal of Remote Sensing, 20(5): 1216-1229.
[10] 毛庆洲. 车载道路快速检测与测量关键技术研究[D]. 武汉: 武汉大学, 2008. MAO Qingzhou. Research on key technique of landborne based pavement rapid test&measurement[D]. Wuhan: Wuhan University, 2008.
[11] 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. 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.
[12] WANG Chisheng, LI Qingquan, ZHU Jiasong, et al. Formation of the 2015 Shenzhen landslide as observed by SAR shape-from-shading[J]. Scientific Reports,2017, 7: 43351.
[13] 国家市场监督管理总局,国家标准化管理委员会. GB/T 39624—2020机载激光雷达水下地形测量技术规范[S]. 北京: 中国标准出版社, 2020. State Administration for Market Regulation, Standardization and Administration. GB/T 39624—2020Technical specification for underwater topographic survey of airborne lidar[S]. Beijing: Standards Press of China, 2020.
[14] WANG Chisheng, SHU Qiqi, WANG Xinyu, et al. A random forest classifier based on pixel comparison features for urban LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 148: 75-86.
[15] 熊智敏. 城市轨道服役状态检测系统关键技术研究[D]. 武汉: 武汉大学, 2017. XIONG Zhimin. Research on key technology of urban track service state detection system[D]. Wuhan: Wuhan University, 2017.
[16] 廖江海. 隧道表观病害快速检测方法[D]. 深圳: 深圳大学, 2020. LIAO Jianghai. Rapid inspection method of tunnel appearance disease[D]. Shenzhen: Shenzhen University, 2020.
[17] 张亮. 地面复杂环境下移动三维测量精度改善方法研究[D]. 武汉: 武汉大学, 2015. ZHANG Liang. Accuracy improvement for mobile mapping in complex ground environments[D]. Wuhan: Wuhan University, 2015.
[18] 陈智鹏. 复杂环境中辅助惯导定位方法及其在精密移动测量中的应用[D]. 武汉: 武汉大学, 2018. CHEN Zhipeng. Aided INS positioning and application in GNSS-denied environment[D]. Wuhan: Wuhan University, 2018.
[19] LI Qingquan, CHEN Zhipeng, HU Qingwu, et al. Laser-aided INS and odometer navigation system for subway track irregularity measurement[J]. Journal of Surveying Engineering, 2017, 143(4): 04017014.
[20] 李德仁, 袁修孝. 误差处理与可靠性理论[M]. 2版. 武汉: 武汉大学出版社, 2012. LI Deren, YUAN Xiuxiao. Error processing and reliability theory[M]. 2nd ed. Wuhan: Wuhan University Press, 2012.
[21] 李德仁. 利用选择权迭代法进行粗差定位[J]. 武汉测绘学院学报, 1984(1): 46-68. LI Deren. Gross error location by means of the iteration method with variable weights[J]. Journal of Wuhan Technical University of Surveying and Mapping, 1984(1): 46-68.
[22] 朱建军, 宋迎春. 现代测量平差与数据处理理论的进展[J]. 工程勘察, 2009, 37(12): 1-5, 21. ZHU Jianjun, SONG Yingchun. Progress of modern surveying adjustment and theory of data processing[J]. Geotechnical Investigation & Surveying, 2009, 37(12): 1-5, 21.
[23] 龚健雅, 季顺平. 摄影测量与深度学习[J]. 测绘学报, 2018, 47(6): 693-704.DOI: 10.11947/j.AGCS.2018.20170640. GONG Jianya, JI Shunping. Photogrammetry and deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 693-704. DOI: 10.11947/j.AGCS.2018.20170640.
[24] ZOU Qin, CAO Yu, LI Qingquan, et al. CrackTree: automatic crack detection from pavement images[J]. Pattern Recognition Letters, 2012, 33(3): 227-238.
[25] ZOU Qin, ZHANG Zheng, LI Qingquan, et al. DeepCrack: learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing, 2019, 28(3): 1498-1512.
[26] LI Qingquan, ZOU Qin, ZHANG Daqiang, et al. FoSA: F^* seed-growing approach for crack-line detection from pavement images[J]. Image and Vision Computing, 2011, 29(12): 861-872.
[27] 邹勤. 低信噪比路面裂缝增强与提取方法研究[D]. 武汉: 武汉大学, 2012. ZOU Qin. Study on enhancement and extraction of low-SNR pavement cracks[D]. Wuhan: Wuhan University, 2012.
[28] ZHANG Dejin, ZOU Qin, LIN Hong, et al. Automatic pavement defect detection using 3D laser profiling technology[J]. Automation in Construction, 2018, 96: 350-365.
[29] HE Li, LIN Hong, ZOU Qin, et al. Accurate measurement of pavement deflection velocity under dynamic loads[J]. Automation in Construction, 2017, 83: 149-162.
[30] 张德津, 李清泉. 公路路面快速检测技术发展综述[J]. 测绘地理信息,2015, 40(1): 1-8. ZHANG Dejin, LI Qingquan. A review of pavement high speed detection technology[J].Journal of Geomatics, 2015, 40(1): 1-8.
[31] MA Hongqi, CHI Fudong. Technical progress on researches for the safety of high concrete-faced rockfill dams[J]. Engineering, 2016, 2(3): 332-339.
[32] 周伟, 花俊杰, 常晓林, 等. 水布垭高面板堆石坝运行期工作性态评价及变形预测[J]. 岩土工程学报, 2011, 33(S1): 65-70. ZHOU Wei, HUA Junjie, CHANG Xiaolin, et al. Estimation of work status and deformation prediction of Shibuya CFRD[J]. Chinese Journal of Geotechnical Engineering, 2011, 33(S1): 65-70.
[33] YIN Xianfei, CHEN Yuan, BOUFERGUENEA, et al. A deep learning-based framework for an automated defect detection system for sewer pipes[J]. Automation in Construction, 2020, 109: 102967.
[34] LI Duanshun, CONG Anran, GUO Shuai. Sewer damage detection from imbalanced CCTV inspection data using deep convolutional neural networks with hierarchical classification[J]. Automation in Construction, 2019, 101: 199-208.
[35] 唐炉亮, 字陈波, 李清泉, 等. 大型水电工程百米级引水竖井的病害检测技术[J]. 测绘学报, 2018, 47(2): 260-268. TANG Luliang, ZI Chenbo, LI Qingquan, et al. Defect detecting technology for over 100-meter shaft[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(2): 260-268.