Citation: | WANG Shi-lei, GAO Yan, QI Fa-lin, KE Zai-tian, LI Hong-yan, LEI Yang, PENG Zhan. Review on inspection technology of railway operation tunnels[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 41-57. doi: 10.19818/j.cnki.1671-1637.2020.05.003 |
[1] |
TIAN Si-ming, GONG Jiang-feng. Statistics of railway tunnels in China as of end of 2019[J]. Tunnel Construction, 2020, 40(2): 292-297. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD202002026.htm
|
[2] |
LU Chun-fang. Maintenance and repair mode and technologies for high speed railway bridges and tunnels[J]. Chinese Railways, 2017(7): 1-8. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TLZG201707001.htm
|
[3] |
Transportation Bureau of China Railway Corporation. Current situation of railway tunnel[J]. Tunnel Construction, 2015(6): 534. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD201506011.htm
|
[4] |
NIU Dao-an. Technology and development of railway infrastructure lifetime inspection[J]. Railway Engineering, 2020, 60(4): 5-8, 16. (in Chinese). doi: 10.3969/j.issn.1003-1995.2020.04.02
|
[5] |
MA Wei-bin, CHAI Jin-fei. Development status of disease detection, monitoring, evaluation and treatment technology of railway tunnels in operation[J]. Tunnel Construction, 2019, 39(10): 1553-1562. (in Chinese). doi: 10.3973/j.issn.2096-4498.2019.10.002
|
[6] |
XIAO Guang-zhi. Discussion on design and construction improvement measures based on current typical diseases of railway tunnel lining[J]. Tunnel Construction, 2018, 38(9): 1416-1422. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD201809005.htm
|
[7] |
SASAMA H, UKAI M, OHTA M, et al. Inspection system for railway facilities using a continuously scanned image[J]. Electrical Engineering in Japan, 1998, 125(2): 52-64. doi: 10.1002/(SICI)1520-6416(19981115)125:2<52::AID-EEJ7>3.0.CO;2-N
|
[8] |
STENT S, GHERARDI R, STENGER B, et al. Visual change detection on tunnel linings[J]. Machine Vision and Applications, 2016, 27(3): 319-330. doi: 10.1007/s00138-014-0648-8
|
[9] |
MIYATA N. Tunnel inspection vehicle equipped with infrared/CCD camera and image processing technology[J]. Concrete Engineering, 2000, 38(1): 79-80. (in Japanese).
|
[10] |
MONTERO R, VICTORES J G, MARTINEZ S, et al. Past, present and future of robotic tunnel inspection[J]. Automation in Construction, 2015, 59: 99-112. doi: 10.1016/j.autcon.2015.02.003
|
[11] |
FUJINO Y, SIRINGORINGO D M. Recent research and development programs for infrastructures maintenance, renovation and management in Japan[J]. Structure and Infrastructure Engineering, 2020, 16(1): 3-25. doi: 10.1080/15732479.2019.1650077
|
[12] |
TABRIZI K, CELAYA M, MILLER B S, et al. Damage assessment of tunnel lining by mobile laser scanning: Pittsburgh, Pennsylvania, implementation phase of FHWA SHRP 2 R06G Project[J]. Transportation Research Record, 2017(2642): 166-179.
|
[13] |
LI Jian-chao, ZHANG Chui-bing, CHAI Xue-song, et al. Research on crack detection system of tunnel lining based on image recognition technology[J]. Railway Engineering, 2018, 58(1): 20-24. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TDJZ201801005.htm
|
[14] |
HUANG Hong-wei, SUN Yan, XUE Ya-dong, et al. Inspection equipment study for subway tunnel defects by grey-scale image processing[J]. Advanced Engineering Informatics, 2017, 32: 188-201. doi: 10.1016/j.aei.2017.03.003
|
[15] |
WANG Ping-rang, HUANG Hong-wei, XUE Ya-dong. Model test study of factors affecting automatic detection performance of cracks in tunnel lining[J]. Chinese Journal of Rock Mechanics and Engineering, 2012, 31(8): 1705-1714. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201208025.htm
|
[16] |
LI Qing-tong, HUANG Hong-wei, XUE Ya-dong, et al. Model test study on factors affecting image sharpness of tunnel lining[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(S2): 3915-3926. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2017S2024.htm
|
[17] |
DOU Hai-tao, HUANG Hong-wei, XUE Ya-dong. Model test on infrared radiation feature of tunnel seepage and image processing[J]. Chinese Journal of Rock Mechanics and Engineering, 2011, 30(S2): 3386-3391. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2011S2005.htm
|
[18] |
ATTARD L, DEBONO C J, VALENTINO G, et al. Tunnel inspection using photogrammetric techniques and image processing: areview[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144: 180-188. doi: 10.1016/j.isprsjprs.2018.07.010
|
[19] |
UKAI M. Development of image processing technique for detection of tunnel wall deformation using continuously scanned image[J]. Quarterly Report of RTRI, 2000, 41(3): 120-126. doi: 10.2219/rtriqr.41.120
|
[20] |
LIU Xue-zeng, YE Kang. A long-distance image measuring technique for crack on tunnel lining[J]. Journal of Tongji University (Natural Science), 2012, 40(6): 829-836. (in Chinese). doi: 10.3969/j.issn.0253-374x.2012.06.005
|
[21] |
WANG Rui, QI Tai-yue, HU Shen, et al. Background processing of tunnel lining crack detection and breakpoint connection algorithm[J]. Journal of Basic Science and Engineering, 2017, 25(4): 742-750. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YJGX201704009.htm
|
[22] |
WANG Ping-rang, HUANG Hong-wei, XUE Ya-dong. Automatic recognition of cracks in tunnel lining based on characteristics of local grids in images[J]. Chinese Journal of Rock Mechanics and Engineering, 2012, 31(5): 991-999. (in Chinese). doi: 10.3969/j.issn.1000-6915.2012.05.016
|
[23] |
PU Bing-rong, QI Tai-yue, HUANG Xiao-dong, et al. Study on characteristics extraction of bifurcated crack of tunnel lining[J]. Railway Standard Design, 2019, 63(10): 135-141. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TDBS201910026.htm
|
[24] |
HUANG Hong-wei, SUN Yan, XUE Ya-dong. Research progress of machine vision based disease detecting techniques for the tunnel lining surface[J]. Modern Tunneling Technology, 2014, 51(S1): 19-31. (in Chinese). https://cpfd.cnki.com.cn/Article/CPFDTOTAL-OGTY201411003003.htm
|
[25] |
MAKANTASIS K, PROTOPAPADAKIS E, DOULAMIS A, et al. Deep convolutional neural networks for efficient vision based tunnel inspection[C]//IEEE. 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). New York: IEEE, 2015: 335-342.
|
[26] |
CHA Y J, CHOI W, BÜYÜKÖZTÜRK O. Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361-378. doi: 10.1111/mice.12263
|
[27] |
XUE Ya-dong, LI Yi-cheng. A method of disease recognition for shield tunnel lining based on deep learning[J]. Journal of Hunan University (Natural Sciences), 2018, 45(3): 100-109. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HNDX201803012.htm
|
[28] |
HUANG Hong-wei, LI Qing-tong. Water leakage image recognition of shield tunnel by deep learning[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(12): 2861-2871. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201712001.htm
|
[29] |
HUANG Hong-wei, LI Qing-tong, ZHANG Dong-ming. Deep learning based image recognition for crack and leakage defects of metro shield tunnel[J]. Tunnelling and Underground Space Technology, 2018, 77: 166-176. doi: 10.1016/j.tust.2018.04.002
|
[30] |
CHAI Xue-song, ZHU Xing-yong, LI Jian-chao, et al. Tunnel lining crack identification algorithm based on deep convolutional neural network[J]. Railway Engineering, 2018, 58(6): 60-65. (in Chinese). doi: 10.3969/j.issn.1003-1995.2018.06.16
|
[31] |
WIMSATT A, WHITE J, LEUNG C, et al. Mapping voids, debonding, delamination, moisture, and other defects behind or within tunnel linings[R]. Washington DC: Transportation Research Board, 2013.
|
[32] |
MATSUNUMA M, SUZUKI T. Verification of tunnel lining inspection car using electromagnetic radar[J]. Construction Project, 2011(736): 34-38. (in Japanese).
|
[33] |
QI Fa-lin, LI Guo-qing, JIANG Bo. Development and application of railway tunnel state inspection vehicle[J]. Chinese Railways, 2013(9): 75-77, 99. (in Chinese). doi: 10.3969/j.issn.1001-683X.2013.09.021
|
[34] |
ZAN Yue-wen, LI Zhi-lin, SU Guo-feng, et al. An innovative vehicle-mounted GPR technique for fast and efficient monitoring of tunnel lining structural conditions[J]. Case Studies in Nondestructive Testing and Evaluation, 2016, 6: 63-69. doi: 10.1016/j.csndt.2016.10.001
|
[35] |
ZAN Yue-wen, SU Guo-feng, WEI Wen-tao, et al. Detection technology of the vehicle-mounted GPR and its application in high-speed railway tunnels[J]. Modern Tunneling Technology, 2018, 55(S2): 1288-1294. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XDSD2018S2164.htm
|
[36] |
YANG Yan-qing, HE Shao-hui, JIANG Bo, et al. Simulation test of GPR detection of integral lining of railway tunnel[J]. Journal of the China Railway Society, 2012, 34(9): 93-98. (in Chinese). doi: 10.3969/j.issn.1001-8360.2012.09.016
|
[37] |
XU Jun. GPR signal automatic identification method and engineering application for multi-type defects in underground structures[D]. Beijing: University of Science and Technology Beijing, 2019. (in Chinese).
|
[38] |
XU Hui. Tunnel lining diseases GPR detection intelligent inversion and identification methods based on deep learning[D]. Jinan: Shandong University, 2019. (in Chinese).
|
[39] |
LALAGÜE A, LEBENS M A, HOFF I, et al. Detection of rockfall on a tunnel concrete lining with ground-penetrating radar (GPR)[J]. Rock Mechanics and Rock Engineering, 2016, 49(7): 2811-2823. doi: 10.1007/s00603-016-0943-y
|
[40] |
YASUDA T, YAMAMOTO H, KITAZAWA R. Tunnel cavity exploration at 50 km·h-1: realization of tunnel lining thickness and cavity exploration by high speed non-contact radar[J]. Construction Machine, 2014, 66(12): 51-56. (in Japanese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK202106008.htm
|
[41] |
XU Xian-lei, XIA Tian, VENKATACHALAM A S, et al. Development of high-speed ultrawideband ground-penetrating radar for rebar detection[J]. Journal of Engineering Mechanics, 2013, 139(3): 272-285. doi: 10.1061/(ASCE)EM.1943-7889.0000458
|
[42] |
YIN De, YE Sheng-bo, LIU Jin-wei, et al. Novel time-domain ultra-wide band tem horn antenna for highway GPR applications[J]. Journal of Radars, 2017, 6(6): 611-618. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX201706006.htm
|
[43] |
SU Guo-feng. Experimental study on vehicle-mounted GPR monitoring high-speed railway tunnel[D]. Chengdu: Southwest Jiaotong University, 2017. (in Chinese).
|
[44] |
XIONG Hong-qiang. Research on synthetic aperture focusing imaging technique of the vehicle-mounted GPR data[D]. Chengdu: Southwest Jiaotong University, 2018. (in Chinese).
|
[45] |
LEI Yang, TIAN Tian. The vibration characteristic and impact analysis of the tunnel lining detection device based on arc rotating multi-section mechanism[J]. Advances in Mechanical Engineering, 2020, 12(4): 1-18.
|
[46] |
KAWAKAMI K, KONISHI S, SHINOHARAH, et al. Infrared thermometry application to the detection of voidin the subway tunnel lining surface[J]. Journal of Japan Society of Civil Engineers F1 (Tunnel Engineering), 2018, 74(1): 25-39. (in Japanese).
|
[47] |
KURAHASHI S, MIKAMI K, KITAMURA T, et al. Demonstration of 25 Hz inspection speed laser remote sensing for internal concrete defects[J]. Journal of Applied Remote Sensing, 2018, 12(1): 1-11.
|
[48] |
MIZUGUCHI T, OHNISHI Y, TOKUDA K, et al. Inspection of the crack measurement of the road tunnel by mobile imaging technology[J]. Journal of Japan Society of Civil Engineers F2 (Underground Space Research), 2017, 73(1): 1-10. (in Japanese).
|
[49] |
MIZUGUCHI T, OHNISHI Y, NISHIYAMA S, et al. Research of maintenance of road tunnel by MIMM[J]. Journal of Japan Society of Civil Engineers F2 (Underground Space Research), 2015, 71(1): 20-30. (in Japanese).
|
[50] |
DUAN Pei-yong, XUE Feng, XIE Jin-mei, et al. Research on the application of laser scanning technology in railway gauge inspection[J]. Railway Engineering, 2013(8): 89-92. (in Chinese). doi: 10.3969/j.issn.1003-1995.2013.08.28
|
[51] |
DU Zhao-yu. Application research of mobile 3D laser scanning system in railway limit measurement[J]. Bulletin of Surveying and Mapping, 2019(S2): 185-187. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB2019S2046.htm
|
[52] |
AI Qing, YUAN Yong, BI Xiang-li. Acquiring sectional profile of metro tunnels using charge-coupled device cameras[J]. Structure and Infrastructure Engineering, 2016, 12(9): 1065-1075. doi: 10.1080/15732479.2015.1076855
|
[53] |
ZHAN Dong, YU Long, XIAO Jian, et al. Multi-camera and structured-light vision system (MSVS) for dynamic high-accuracy 3D measurements of railway tunnels[J]. Sensors, 2015(15): 8664-8684.
|
[54] |
WANG Xue-mei, NI Wen-bo. Measurement foundation of railway track geometrical parameters based on strapdown inertial technique[J]. Journal of Southwest Jiaotong University, 2012, 47(3): 355-360. (in Chinese). doi: 10.3969/j.issn.0258-2724.2012.03.001
|
[55] |
LI Sheng, HU Wen-bin, YANG Yan, et al. Research of fog-based measurement technique for continuous curve modes of long span bridge[J]. Bridge Construction, 2014, 44(5): 69-74. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-QLJS201405015.htm
|
[56] |
GAN Wei-bing, HU Wen-bin, LIU Fang, et al. Bridge continuous deformation measurement technology based on fiber optic gyro[J]. Photonic Sensors, 2016, 6(1): 71-77. doi: 10.1007/s13320-015-0276-6
|
[57] |
EIJI T, NAOKAZU S, KENICHI A, et al. Three dimensional profile measurement system for tunnel surface using 1.3 mega-pixels high speed image processing camera[J]. Journal of Control, Measurement, and System Integration, 2012, 48(12): 863-871. (in Japanese).
|
[58] |
MENENDEZ E, VICTORES J G, MONTERO R, et al. Tunnel structural inspection and assessment using an autonomous robotic system[J]. Automation in Construction, 2018, 87: 117-126. doi: 10.1016/j.autcon.2017.12.001
|