Review of state-of-the-art in structural health monitoring of tunnel engineering

Weizhong Chen , Xu-Yan Tan , Jianping Yang

Smart Underground Engineering ›› 2025, Vol. 1 ›› Issue (1) : 40 -50.

PDF (2925KB)
Smart Underground Engineering ›› 2025, Vol. 1 ›› Issue (1) : 40 -50. DOI: 10.1016/j.sue.2025.05.004
Original article
research-article

Review of state-of-the-art in structural health monitoring of tunnel engineering

Author information +
History +
PDF (2925KB)

Abstract

Structural health-monitoring (SHM) systems are essential for ensuring the safety, reliability, and longevity of tunnel structures. This paper reviews the application of SHM systems in tunnels based on examples from 51 projects worldwide that have implemented such systems. Commonly used monitoring indicators and their classifications are outlined, and the selection criteria for these indicators under different operating conditions are clarified. Additionally, it summarizes the main monitoring and measurement technologies and introduces methods for preprocessing and analyzing large-scale monitoring data. The current limitations of SHM systems are discussed and future research directions to address these gaps are proposed. The objective of this study is to provide a complete guide for enhancing and innovating SHM systems in tunnel engineering.

Keywords

Tunnel / Structural health monitoring / Monitoring indicators / Big data / Sensor

Cite this article

Download citation ▾
Weizhong Chen, Xu-Yan Tan, Jianping Yang. Review of state-of-the-art in structural health monitoring of tunnel engineering. Smart Underground Engineering, 2025, 1(1): 40-50 DOI:10.1016/j.sue.2025.05.004

登录浏览全文

4963

注册一个新账户 忘记密码

CRediT authorship contribution statement

Weizhong Chen: Writing -original draft, Resources, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. Xu-Yan Tan: Writing -original draft, Visualization, Validation, Data curation. Jianping Yang: Writing -review & editing, Validation, Supervision, Resources, Methodology.

Declaration of competing interests

Weizhong Chen is an Associate Editor for this journal, and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

Acknowledgements

This study was supported by the “National Key Research and Development Program of China” (2024YFF0508203 and 2021YFC3100805) and the “Major Program of the National Natural Science Foundation of China (grant no. 42293355).”

References

[1]

J. Shi, H. Huang, J. Shen, X. Bao, Robust sensor placement method for deformation monitoring in shield tunnels, J. Huazhong Univ Sci. Technol. (Nat. Scie. Edition) (2024) Preprint, doi: 10.13245/j.hust.250276.

[2]

H. Huang, H. Shao, D. Zhang, Wang F, Deformational responses of operated shield tunnel to extreme surcharge: a case study, Struct. Infrastruct. Eng. 13 (2017) 345-360, doi: 10.1080/15732479.2016.1170156.

[3]

M. Liu, R. Yu, Y. Li, R. Chen, Y. Sun, Design and engineering practice of structural health monitoring for super-large diameter shield tunnel, Spec. Struct. 41 (2024) 82-88, doi: 10.19786/j.tzjg.2024.04.014.

[4]

S. Bhalla, Y.W. Yang, J. Zhao, C.K. Soh, Structural health monitoring of under-ground facilities-technological issues and challenges, Tunn. Undergr. Space Technol. 20 (2005) 487-500, doi: 10.1016/j.tust.2005.03.003.

[5]

H. Li, D. Li, G. Song, Recent applications of fiber optic sensors to health monitoring in civil engineering, Eng. Struct. 26 (2004) 1647-1657, doi: 10.1016/j.engstruct.2004.05.018.

[6]

W. Chen, C. Li, C. Zeng, J. Yang, J. Liu, Establishment and application of structural health monitoring system for large shield tunnel, Chin. J. Rock Mech. Eng. 37 (2018) 1-13, doi: 10.13722/j.cnki.jrme.2017.0327.

[7]

H.S. Chung, B.S. Chun, B.H. Kim, Y.J. Lee, Measurement and analysis of long-term behavior of Seoul metro tunnels using the automatic tunnel monitoring systems, Tunn. Undergr. Space Technol. 21 (2006) 316, doi: 10.1016/j.tust.2005.12.032.

[8]

S. Sha, X. Liu, S. Wang, Health monitoring and safety evaluation of underwater shield tunnels using fiber Bragg grating sensors, Tunn. Constr. 44 (2024) 485, doi: 10.3973/j.issn.2096-4498.2024.S1.052.

[9]

J. Ou, H. Li, Structural health monitoring in mainland China: Review and future trends, Struct. Health Monit. 9 (2010) 219-231, doi: 10.1177/1475921710365269.

[10]

Z. Zheng, Y. Lei, Structural monitoring techniques for the largest excavation section subsea tunnel: Xiamen Xiang’an Subsea Tunnel, J. Aerosp. Eng. 30 (2017) B4016002, doi: 10.1061/(ASCE)AS.1943-5525.0000594.

[11]

S. Sha, X. Liu, S. Wang, Health monitoring and safety evaluation of underwater shield tunnels using fiber Bragg grating sensors, Tunn. Constr. 44 (2024) 485. 10, doi: 10.3973/j.issn. 2096-4498.2024.S1.052.

[12]

J. Su, D. Zhang, X. Niu, Q. Fang, Research on design of subsea tunnel structural health monitoring, Chin. J. of Rock Mech. Eng. 26 (2007) 3785-3792.

[13]

X. Tang, Z. Zhang, X. Chen, X. Zhang, Current status and prospects of underwater shield tunnel structural health monitoring system, Mod. Transportation Techno. 17 (2020) 33-38.

[14]

B. Zhang, Health monitoring and data analysis of large diameter shield tunnel in expansive soil layer, Railw. Constr. Technol. 07 (2018) 60-64.

[15]

T. Huang, J. Sun, J. Tao, Y. Huang, Nat. Sci (Ed.), Subsidence monitoring and ana-lyzing m subway tunnel construction, J. Southeast Univ. 36 (2006) 262-266 Ed.

[16]

G. Hou, Z. Li, T. Hu, T. Zhou, H. Xiao, K. Wang, J. Hu, J. Zhu, Study of tunnel settlement monitoring based on distributed optic fiber strain sensing technology, Rock Soil Mech. 46 (2013) 104-116, doi: 10.16285/j.rsm.2019.1929.

[17]

P.J. Bennett, Y. Kobayashi, K. Soga, P. Wright, Wireless sensor network for moni-toring transport tunnels, Proc. Inst. Civ. Eng. Geotech. Eng. 163 (2010) 147-156, doi: 10.1680/geng.2010.163.3.147.

[18]

J. Gómez, J.R. Casas, S. Villalba, Structural health monitoring with distributed op-tical fiber sensors of tunnel lining affected by nearby construction activity, Autom. Constr. 119 (2020) 103261, doi: 10.1016/j.autcon.2020.103261.

[19]

C.Y. Gue, M. Wilcock, M.M. Alhaddad, M.Z.E.B. Elshafie, K. Soga, R.J. Mair, The monitoring of an existing cast iron tunnel with distributed fibre op-tic sensing (DFOS), J. Civil Struct. Health Monit. 5 (2015) 573-586, doi: 10.1007/s13349-015-0109-8.

[20]

Fajkus M J. Nedoma P. Mec E. Hrubesova R. Martinek V. Vasinek, Analysis of the highway tunnels monitoring using an optical fiber implemented into primary lining, J. Electr. Eng. 68 (2017) 364-370, doi: 10.1515/jee-2017-0068.

[21]

M. Kristof, S. Wim, F. Gerrit, S. Koen, F. Stijn, Anomaly detection in long-term tunnel deformation monitoring, Eng. Struct. 250 (2022) 113383, doi: 10.1016/j.engstruct.2021.113383.

[22]

G. Bossi, L. Schenato, G. Marcato, Structural health monitoring of a road tunnel intersecting a large and active landslide, Appl. Sci. 7 (2017) 1271, 10.3390/app7121271.

[23]

H. Yun, S. Park, N. Mehdawi, S. Mokhtari, M. Chopra, L.N. Reddi, K. Park, Monitor-ing for close proximity tunneling effects on an existing tunnel using principal com-ponent analysis technique with limited sensor data, Tunn. Undergr. Space Technol. 43 (2014) 398-412, doi: 10.1016/j.tust.2014.06.003.

[24]

S. Dalgı ç, Tunneling in squeezing rock, the Bolu tunnel, Anatolian Motorway, Turkey, Eng. Geol. 67 (2002) 73-96, doi: 10.1016/S0013-7952(02)00146-1.

[25]

C.M. Monsberger, W. Lienhart, Distributed fiber optic shape sensing along shotcrete tunnel linings: Methodology, field applications, and monitoring results, J. Civil Struct. Health Monit. 11 (2021) 337-350, doi: 10.1007/s13349-020-00455-8.

[26]

C. Wang, M. Friedman, Z.L. Li, Monitoring and assessment of a cross-passage twin tunnel long-term performance using wireless sensor network, Can. Geotech. J. 60 (2023) 1140-1160, doi: 10.1139/cgj-2022-0224.

[27]

M. Čokorilo Ili ć, A. Mladenovi ć, M. Ćuk, I. Jemcov, The importance of detailed groundwater monitoring for underground structure in karst (case study: HPP Pirot, Southeastern Serbia), Water. (Basel) 11 (2019) 603, 10.3390/w11030603.

[28]

C.M. Monsberger, P. Bauer, F. Buchmayer, W. Lienhart, Large-scale dis-tributed fiber optic sensing network for short and long-term integrity moni-toring of tunnel linings, J. Civil Struct. Health Monit. 12 (2022) 1317-1327, doi: 10.1007/s13349-022-00560-w.

[29]

M. Ghorbani, M. Sharifzadeh, S. Yasrobi, M. Daiyan, Geotechnical, structural and geodetic measurements for conventional tunnelling hazards in urban areas-The case of Niayesh road tunnel project, Tunn. Undergr. Space Technol. 31 (2012) 1-8, doi: 10.1016/j.tust.2012.02.009.

[30]

J.S. Sharma, A.M. Hefny, J. Zhao, C.W Chan, Effect of large excavation on defor-mation of adjacent MRT tunnels, Tunn. Undergr. Space Technol. 16 (2001) 93-98, doi: 10.1016/S0886-7798(01)00033-5.

[31]

X. Zhang, W. Broere, Design of a distributed optical fiber sensor system for measur-ing immersed tunnel joint deformations, Tunn. Undergr. Space Technol. 131 (2023) 104770, doi: 10.1016/j.tust.2022.104770.

[32]

K.F. Bizjak, B. Petkovš ek, Displacement analysis of tunnel support in soft rock around a shallow highway tunnel at Golovec, Eng. Geol. 75 (2004) 89-106, doi: 10.1016/j.enggeo.2004.05.003.

[33]

G.R. Schneider, A. Garcia-Fontanet, A. Ledesma, R. Raveendra T. P. Orodea, Toronto-York Spadina subway extension tunnelling under Schulich Building, Can. J. Civ. Eng. 46 (2019) 87-103, doi: 10.1139/cjce-2017-0062.

[34]

A. Unlutepe, H. Ozener, Geotechnical measurements at Izmir LRT system tunnels, Tunn. Undergr. Space Technol 23 (2008) 734-741, doi: 10.1016/j.tust.2007.12.004.

[35]

A. Bandini, P. Berry, D. Boldini, Tunnelling-induced landslides: the Val di Sambro tunnel case study, Eng. Geol. 196 (2015) 71-87, doi: 10.1016/j.enggeo.2015.07.001.

[36]

M.S. Kova čevi ć, M. Ba či ć, K. Gavin, I. Stipanovi ć, Assessment of long-term deformation of a tunnel in soft rock by utilizing particle swarm opti-mized neural network, Tunn. Undergr. Space Technol. 110 (2021) 103838, doi: 10.1016/j.tust.2021.103838.

[37]

J. Chen, X. Feng, H. Wei, Statistics and analysis of underwater tun-nels in China (by the end of 2023), Tunn. Constr. 44 (2024) 826-881, doi: 10.3973/j.issn.2096-4498.2024.04.020.

[38]

H. Dai, Y. Ji, Statistical analysis of Chinese large-diameter shield tunnel and state-of-art and prospective of comprehensive technologies, Tunn. Constr. 42 (2022) 757-783, doi: 10.3973/j.issn.2096-4498.2022.05.002.

[39]

Z. Li, N. Xu, Z. Sun, B. Li, J. Liu, Y. Sun, J. Zhu, Analysis of large deformation characteristics of soft rock tunnel surrounding rock under high geo-stresses based on microseismic monitoring and numerical simulation, Chin. J.l Rock Mech. Eng. 43 (2024) 2725-2737, doi: 10.13722/j.cnki.jrme.2023.1104.

[40]

Y. Sun, H. Su, P. Xiao, P. Li, B. Li, X. Zhou, K. Bian, N. Xu, Visualiza-tion and early warning analysis of damage degree of surrounding rock mass in underground powerhouse, Int. J. Min. Sci. Technol. 33 (2023) 717-731, doi: 10.1016/j.ijmst.2022.12.011.

[41]

L. Wang, Q. Wang, S. Li, N. Xu, R. Pan, M. He, Y. Xu, H. Zhang, Stability analysis and characteristic of seismic activity during roadway development in soft rock, J. Min. Saf. Eng. 35 (2018) 10-18 http://ckxb.cumt.edu.cn/CN/Y2018/V35/I1/10.

[42]

R. Xing, S. Jiang, P. Xu, Long-term temperature monitoring of tunnel in high-cold and high-altitude area using distributed temperature monitoring system, Meas. 94 (2017) 456-464, doi: 10.1016/j.measurement.2016.10.032.

[43]

X. Qiao, X. Yang, Y. Feng, Current status and prospects of research on tunnel frost damage in high-altitude and severely cold region, Tunn. Constr. 44 (2024) 233-256, doi: 10.3973/j.issn.2096-4498.2024.02.003.

[44]

X.Y. Tan, W.Z. Chen, L.Y. Wang, J.P. Yang, The impact of uneven temperature dis-tribution on stability of concrete structures using data analysis and numerical ap-proach, Adv. Struct. Eng. 24 (2021) 279-290, doi: 10.1177/1369433220950610.

[45]

D. Hauswirth, A.M. Puzrin, A. Carrera, J.R. standing, M.S.P. Wan, Use of fibre-optic sensors for simple assessment of ground surface displacements during tunnelling, Géotech 64 (2014) 837-842, doi: 10.1680/geot.14.T.009.

[46]

Y. Ding, B. Shi, D. Zhang, Data processing in BOTDR distributed strain mea-surement based on pattern recognition, Optik. (Stuttg) 121 (2010) 2234-2239, doi: 10.1016/j.ijleo.2009.12.004.

[47]

Y. Wang, L. Ezra, Z.Q. Pan, T. Wang, Single-end simultaneous temperature and strain sensing techniques based on Brillouin optical time domain reflectometry in few-mode fibers, Opt. Express. 23 (2015) 9024-9039, doi: 10.1364/OE.23.009024.

[48]

H. Mohamad, P.J. Bennett, K. Soga, J.M. Robert, C.S. Lim, C.K. Knight-Hassell, C.N. Ow, Monitoring tunnel deformation induced by close-proximity bored tunneling dis-tributed optical fiber using strain, 7th FMGM 2007: field measurements in geome-chanics. (2007) 1-13. https://doi.org/10.1061/40940(307)84.

[49]

L.L.K. Cheung, K. Soga, P.J. Bennett, Y. Kobayashi, B. Amatya, P. Wright, Optical fiber strain measurement for tunnel lining monitoring, Proc. Inst. Civ. Eng. Geotech. Eng. 163 (2010) 119-130, doi: 10.1680/geng.2010.163.3.119.

[50]

B. Glisic, D. Inaudi. Fibre optic Methods for Structural Health Monitoring, Wiley-Interscience, New York, 2007.

[51]

B. Shi, X. Xu, D. Wang, H. Cui, Study on botdr-based distributed optical fiber strain measurement for tunnel health diagnosis, Chin. J. Rock Mech. Eng. 224 (2005) 2622-2628, doi: 10.3321/j.issn:1000-6915.2005.15.004.

[52]

X. Zhao, H. Qiu, Application of fiber Bragg grating sensing technol-ogy to tunnel monitoring, Chin. J. Rock Mech. Eng. 26 (2007) 587-593, doi: 10.3321/j.issn:1000-6915.2007.03.021.

[53]

J. Han, J. Guo, Y. Jiang, Monitoring tunnel profile by means of multi-epoch dis-persed 3D LiDAR point clouds, Tunn. Undergr. Space Technol. 33 (2013) 186-192, doi: 10.1016/j.tust.2012.08.008.

[54]

S. Fekete, M. Diederichs, M. Lato, Geotechnical and applications for three-dimensional operational laser scanning in drill and blast tunnels, Tunn. Undergr. Space Technol. 25 (2010) 614-628, doi: 10.1016/j.tust.2010.04.008.

[55]

L. Zhang, X. Cong, S. Zhao, Application of mobile 3D laser scanning tech-nology in tunnel structure monitoring, Bull. Surv. Mapp. (2020) 153-156 http://tb.chinasmp.com/EN/Y2020/V0/I8/153.

[56]

M.A. Chen. Research on Mass Data Processing and Application of Laser Scanning in Subway Shield Tunnel, M.S. Thesis, Beijing Jiaotong University, 2016.

[57]

X. Xue, Y. Xie, X. Zhou, Study on the life-cycle health monitoring technol-ogy of water-rich loess tunnel, Adv. Mater. Sci. Eng. 2019 (2019) 9461890, 10.1155/2019/9461890.

[58]

J. Yang, W. Chen, M. Li, X. Tan, J. Yu, Structural health monitoring and analysis of an underwater TBM tunnel, Tunn. Undergr. Space Technol. 82 (2018) 235-247, doi: 10.1016/j.tust.2018.08.053.

[59]

A.M. Alani, F. Tosti, GPR applications in structural detailing of a major tunnel using different frequency antenna systems, Constr. Build. Mater. 158 (2018) 1111-1122, doi: 10.1016/j.conbuildmat.2017.09.100.

[60]

M. Lato, J. Kemeny, R.M. Harrap, Rock bench: Establishing a common repository and standards for assessing rockmass characteristics using LiDAR and photogrammetry, Comput. Geosci. 50 (2013) 106-114, doi: 10.1016/j.cageo.2012.06.014.

[61]

F. Zhang, X. Xie, H. Huang, Application of ground penetrating radar in grouting evaluation for shield tunnel construction, Tunn. Undergr. Space Technol. 25 (2010) 99-107, doi: 10.1016/j.tust.2009.09.006.

[62]

H. Huang, Q. Li, D. Zhang, Deep learning based image recognition for crack and leakage defects of metro shield tunnel, Tunn. Undergr. Space Technol. 77 (2018) 166-170, doi: 10.1016/j.tust.2018.04.002.

[63]

X. Tan, W. Chen, J. Yang, L. Wang, Stability evaluation of concrete structure consid-ering the local damage using nondestructive detection and numerical analysis, The 6th International Conference on Environmental Science and Civil Engineering, IOP Publishing Ltd, 2020, doi: 10.1088/1755-1315/455/1/012119.

[64]

Z. Sun, T. Qian, Y. Ren, C. Chu, Z. Zhou, Study on key technologies and application of engineering big data management platform of tunnel boring machine, Tunn. Constr. 40 (2020) 783, doi: 10.3973/j.issn.2096-4498.2020.06.002.

[65]

Intelligent tunnel automatic monitoring system, Mod. Tunn. Technol. 56 (2019) 228.

[66]

L. Mao, H. Zhai, S. Yin, Discussion on the application of 5G in the rail transportation industry, Mobile Commun. 44 (2020) 63-70.

[67]

C.M. Bishop. Pattern Recognition and Machine Learning, springer, New York, 2006.

[68]

Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nat. 521 (2015) 436-444, doi: 10.1038/nature14539.

[69]

S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9 (1997) 1735-1780, doi: 10.1162/neco.1997.9.8.1735.

[70]

H. Li, Y. Bao, S. Li, D. Zhang, Data science and engineering for structural health mon-itoring, Eng. Mech. 32 (2015) 1-7, doi: 10.6052/j.issn.1000-4750.2014.08.ST11.

[71]

Y.Q. Bao, H. Li, Machine learning paradigm for structural health monitoring, Struct. Health Monit. 20 (2021) 1353-1372, doi: 10.1177/1475921720972416.

[72]

X. Tan, W. Chen, G. Wu, L. Wang, J. Yang, A structural health monitoring system for data analysis of segment joint opening in an underwater shield tunnel, Struct. Health Monit. 19 (2020) 1032-1050, doi: 10.1177/1475921719876045.

[73]

X. Tan, W. Chen, J. Yang, X. Tan, Temporal-spatial coupled model for multi-prediction of tunnel structure: using deep attention-based temporal convolutional network, J. Civ. Struct. Health Monit. 12 (2022) 675-687, doi: 10.1007/s13349-022-00574-4.

[74]

X. Tan, S. Palaiahnakote, W. Chen, K. Cheng, B. Du, A novel autoencoder for struc-tural anomalies detection in river tunnel operation, Expert. Syst. Appl. 244 (2024) 122906, doi: 10.1016/j.eswa.2023.122906.

[75]

X. Tan, W. Chen, L. Wang, C. Qin, Spatial deduction of mining-induced stress re-distribution using an optimized non-negative matrix factorization model, J. Rock Mech. Geotech. Eng. 15 (2023) 2868-2876, doi: 10.1016/j.jrmge.2022.12.008.

[76]

J. Sun, H. Wen, Application of artificial intelligence science to construction defor-mation prediction and control of underground engineering in soft soil: cases study on theoretical foundation, method application and fine intelligent technical man-agement, Tunn. Constr. 40 (2020) 1-8, doi: 10.3973/j.issn.2096-4498.2020.01.001.

[77]

X. Tan, Y. Wang, B. Du, J. Ye, W. Chen, L. Sun, L. Li, Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data, Struct. Health Monit. 21 (2022) 1805-1818, doi: 10.48550/arXiv.2109.13167.

[78]

X. Tan, W. Chen, L. Wang, W. Ye, Development of an optimization model for a monitoring point in tunnel stress deduction using a machine learning algorithm, Deep Undergr. Sci. Eng. 4 (2024) 35-45, doi: 10.1002/dug2.12076.

[79]

X. Tan, W. Chen, X. Tan, C. Fan, Y. Mao, K. Cheng, B. Du, Missing data imputation in tunnel monitoring with a spatio-temporal correlation fused ma-chine learning model, J. Civ. Struct. Health Monit. 15 (2025) 1337-1348, doi: 10.1007/s13349-024-00877-8.

[80]

B. Chang, Research on Identification and Analysis Methods of Highway Tunnel Lin-ing Crack Based on Deep Learning, M.S. Thesis, Chongqing Jiaotong University, 2024. https://doi.org/10.27671/d.cnki.gcjtc.2023.000888.

AI Summary AI Mindmap
PDF (2925KB)

1460

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/