Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Abstract

In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

Graphical abstract

Keywords

concrete structure / GPR / damage classification / convolutional neural network / transfer learning

References

[1] Showkati A, Salari-rad H, Hazrati Aghchai M. Predicting long-term stability of tunnels considering rock mass weathering and deterioration of primary support. Tunnelling and Underground Space Technology, 2021, 107 : 103670

[2] Ye F, Qin N, Liang X, Ouyang A, Qin Z, Su E. Analyses of the defects in highway tunnels in China. Tunnelling and Underground Space Technology, 2021, 107 : 103658

[3] Kim K H, Park N H, Kim H J, Shin J H. Modelling of hydraulic deterioration of geotextile filter in tunnel drainage system. Geotextiles and Geomembranes, 2020, 48( 2): 210–219

[4] Gao C, Zhou Z, Yang W, Lin C, Li L, Wang J. Model test and numerical simulation research of water leakage in operating tunnels passing through intersecting faults. Tunnelling and Underground Space Technology, 2019, 94 : 103134

[5] Xiong L, Zhang D, Zhang Y. Water leakage image recognition of shield tunnel via learning deep feature representation. Journal of Visual Communication and Image Representation, 2020, 71 : 102708

[6] Xuefu Z, Yaonan Z. Study on a new-styled measure for treating water leakage of the permafrost tunnels. Tunnelling and Underground Space Technology, 2006, 21( 6): 656–667

[7] ITA Working Group on Maintenance, Repair of Underground Structures. Report on the damaging effects of water on tunnels during their working life. Tunnelling and Underground Space Technology, 1991, 6( 1): 11–76

[8] Luo Y, Chen J. Research status and progress of tunnel frost damage. Journal of Traffic and Transportation Engineering (English Edition), 2019, 6( 3): 297–309

[9] Wang W L, Wang T T, Su J J, Lin C H, Seng C R, Huang T H. Assessment of damage in mountain tunnels due to the Taiwan (China) Chi-Chi Earthquake. Tunnelling and Underground Space Technology, 2001, 16( 3): 133–150

[10] Lei M, Liu L, Shi C, Tan Y, Lin Y, Wang W. A novel tunnel-lining crack recognition system based on digital image technology. Tunnelling and Underground Space Technology, 2021, 108 : 103724

[11] Kim B, Cho S. Automated vision-based detection of cracks on concrete surfaces using a deep learning technique. Sensors (Switzerland), 2018, 18( 10): 3452

[12] Zhu J, Song J. An intelligent classification model for surface defects on cement concrete bridges. Applied Sciences (Basel, Switzerland), 2020, 10( 3): 972

[13] Feng C, Zhang H, Wang S, Li Y, Wang H, Yan F. Structural damage detection using deep convolutional neural network and transfer learning. KSCE Journal of Civil Engineering, 2019, 23( 10): 4493–4502

[14] Song Q, Wu Y, Xin X, Yang L, Yang M, Chen H, Liu C, Hu M, Chai X, Li J. Real-time tunnel crack analysis system via deep learning. IEEE Access : Practical Innovations, Open Solutions, 2019, 7 : 64186–64197

[15] Patterson B, Leone G, Pantoja M, Behrouzi A. Deep learning for automated image classification of seismic damage to built infrastructure. In: 11th US National Conference on Earthquake Engineering, Integrating Science, Engineering & Policy. 2018, 10: 6561–6571

[16] Cha Y J, Choi W, Suh G, Mahmoudkhani S, Büyüköztürk O. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civil and Infrastructure Engineering, 2018, 33( 9): 731–747

[17] Deisseroth K, Gradinaru V. Advances in neurotechniques: Methods that reveal the structure and function of the brain. Science, 2014, 345( 6197): 698–698

[18] Alani A M, Tosti F. GPR applications in structural detailing of a major tunnel using different frequency antenna systems. Construction & Building Materials, 2018, 158 : 1111–1122

[19] Feng D, Wang X, Zhang B. Specific evaluation of tunnel lining multi-defects by all-refined GPR simulation method using hybrid algorithm of FETD and FDTD. Construction & Building Materials, 2018, 185 : 220–229

[20] Dawood T, Zhu Z, Zayed T. Deterioration mapping in subway infrastructure using sensory data of GPR. Tunnelling and Underground Space Technology, 2020, 103 : 103487

[21] Al-Nuaimy W, Huang Y, Nakhkash M, Fang M T C, Nguyen V T, Eriksen A. Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition. Journal of Applied Geophysics, 2000, 43( 2-4): 157–165

[22] NakkiranP. More data can hurt for linear regression: Sample-wise double descent. 2019, arXiv: 1912.07242

[23] Nakkiran P, Kaplun G, Bansal Y, Yang T, Barak B, Sutskever I. Deep double descent: Where bigger models and more data hurt. Journal of Statistical Mechanics: Theory and Experiment, 2021, 2021( 12): 124003

[24] Rawat W, Wang Z. Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation, 2017, 29( 9): 2352–2449

[25] Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Fei-Fei L. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 2015, 115( 3): 211–252

[26] MarkoffJ. For Web Images, Creating New Technology to Seek and Find. New York Times, 2012

[27] ParkinsonGÉkesC. Ground penetrating radar evaluation of concrete tunnel linings. In: 12th International Conference on Ground Penetrating Radar. Birmingham: University of Birmingham, 2008

[28] Grandjean G, Gourry J C, Bitri A. Evaluation of GPR techniques for civil-engineering applications: Study on a test site. Journal of Applied Geophysics, 2000, 45( 3): 141–156

[29] Kilic G, Eren L. Neural network based inspection of voids and karst conduits in hydro–electric power station tunnels using GPR. Journal of Applied Geophysics, 2018, 151 : 194–204

[30] TrelaCKindTSchubertM. Detection of air voids in concrete by radar in transmission mode. In: 8th International Workshop on Advanced Ground Penetrating Radar (IWAGPR). Florence: IEEE, 2015

[31] JawS WHashimM. Accuracy of data acquisition approaches with ground penetrating radar for subsurface utility mapping. In: 2011 IEEE International RF & Microwave Conference. Seremban: IEEE, 2011

[32] Shao W, Bouzerdoum A, Phung S L, Su L, Indraratna B, Rujikiatkamjorn C. Automatic classification of ground-penetrating-radar signals for railway-ballast assessment. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49( 10): 3961–3972

[33] Torrione P A, Morton K D, Sakaguchi R, Collins L M. Histograms of oriented gradients for landmine detection in ground-penetrating radar data. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52( 3): 1539–1550

[34] Davis A G, Lim M K, Petersen C G. Rapid and economical evaluation of concrete tunnel linings with impulse response and impulse radar non-destructive methods. NDT & E International, 2005, 38( 3): 181–186

[35] Cardarelli E, Marrone C, Orlando L. Evaluation of tunnel stability using integrated geophysical methods. Journal of Applied Geophysics, 2003, 52( 2−3): 93–102

[36] JoHNaY HSongJ B. Data augmentation using synthesized images for object detection. In: 17th International Conference on Control, Automation and Systems (ICCAS). Jeju: IEEE, 2017

[37] Zhong Z, Zheng L, Kang G, Li S, Yang Y. Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34( 7): 13001–13008

[38] Hussain Z, Gimenez F, Yi D, Rubin D. Differential data augmentation techniques for medical imaging classification tasks. AMIA annual symposium proceedings, 2017, 2017 : 979–984

[39] HeKZhangXRenSSunJ. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE, 2016

[40] Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59( 1): 345–359

[41] Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59( 2): 433–456

[42] VenturaA. Extraction of frequent semantic relationships between objects in segmented images. Dissertation for the Doctoral Degree. Turin: Polytechnic University of Turin, 2020 (In Italian)

[43] LongadgeRDongreS. Class imbalance problem in data mining review. 2013, arXiv:1305.1707

[44] Rodriguez J D, Perez A, Lozano J A. Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32( 3): 569–575

[45] RefaeilzadehPTangLLiuH. Cross-Validation. In: Encyclopedia of Database Systems. New York: Springer, 2016

[46] JamesGWittenDHastieTTibshiraniR. An Introduction to Statistical Learning: With Applications in R. New York: Springer, 2013

RIGHTS & PERMISSIONS

The Author(s) 2022. This article is published with open access at link.springer.com and journal.hep.com.cn