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http://dx.doi.org/10.12989/smm.2021.8.3.257

Field implementation of low-cost RFID-based crack monitoring using machine learning  

Fils, Pierredens (Department of Civil and Environmental Engineering, University of Connecticut)
Jang, Shinae (Department of Civil and Environmental Engineering, University of Connecticut)
Sherpa, Rinchen (Department of Civil and Environmental Engineering, University of Connecticut)
Publication Information
Structural Monitoring and Maintenance / v.8, no.3, 2021 , pp. 257-278 More about this Journal
Abstract
As civil infrastructure continues to age, the extension of service life has become a financially attractive solution due to cost savings on reconstruction projects. Efforts to increase the service life of structures include non-destructive evaluation (NDE) and structural health monitoring (SHM) techniques. Nonetheless, visual inspection is more frequently used due to high equipment cost from other techniques and federal biennial inspection requirement. Recently, low-cost Radio Frequency Identification Devices (RFID) have drawn attention for crack monitoring; however, it was yet to be implemented in the field. This paper presents a crack monitoring algorithm using a developed RFID-based sensing system employing machine learning under temperature variations for field implementation. Two reinforced concrete buildings were used as testbeds: a parking garage, and a residential building with crumbling foundation phenomenon. An Artificial Neural Network (ANN)-based crack monitoring architecture is developed as the machine learning algorithm and the results are compared to a baseline model. The results show promise for field implementation of crack monitoring on building structures.
Keywords
crack detection; crumbling foundation; machine learning; residential building; RFID; structural health monitoring;
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1 Unistress (1998), Unistress University of Connecticut Garage, Storrs, CT, USA. https://www.unistresscorp.com/portfolio/uconn/
2 Xia, Z.H. and Curtin, W.A. (2007), "Modeling of mechanical damage detection in CFRPs via electrical resistance", Compos. Sci. Technol., 67(7-8), 1518-1529. https://doi.org/10.1016/j.compscitech.2006.07.017   DOI
3 Dormehl, L. (2019), "What is an Artificial Neural Network? Here's Everything You Need to Know", In: Digital Trends, Bristol, UK. https://www.digitaltrends.com/cool-tech/what-is-an-artificial-neural-network/
4 Federal Highway Administration (2001), Reliability of Visual Inspection for Highway Bridges: FHWA-RD-01-020. https://www.fhwa.dot.gov/publications/research/nde/pdfs/01020a.pdf
5 Holleran, L. (2020), "Crumbling foundations", Connecticut State Department of Housing, Hartford, CT, USA. https://portal.ct.gov/DOH/DOH/Programs/Crumbling-Foundations
6 Leonel, J. (2019), Hyperparameters in Machine/Deep Learning; Sao Paulo, Brazil. https://medium.com/@jorgesleonel
7 Xu, Y., Dong, L., Wang, H., Xie, X. and Wang, P. (2020), "Surface crack detection and monitoring in metal structure using RFID tag", Sensor Review, 40(1), 81-88. https://doi.org/10.1108/sr-06-2019-0153   DOI
8 Amajama, J. (2016), "Impact of weather components on (UHF) radio signal", Int. J. Eng. Res. General Sci., 4(3), 481-494.
9 Park, G., Sohn, H., Farrar, C.R. and Inman, D.J. (2003), "Overview of piezoelectric impedance-based health monitoring and path forward", Shock Vib. Digest, 35(6), 451-464.   DOI
10 Muller, M., Mitton, D., Talmant, M., Johnson, P. and Laugier, P. (2008), "Nonlinear ultrasound can detect accumulated damage in human bone", Journal of Biomech., 41(5), 1062-1068. https://doi.org/10.1016/j.jbiomech.2007.12.004   DOI
11 Marindra, A.M.J., Sutthaweekul, R. and Tian, G.Y. (2018), "Depolarizing chipless RFID sensor tag for characterization of metal cracks based on dual resonance features", In: International Conference on Information Technology and Electrical Engineering: Smart Technology for Better Society, ICITEE 2018, 8534943, 73-78. https://doi.org/10.1109/iciteed.2018.8534943   DOI
12 Xu, Y., Dong, L., Wang, H., Di, Y., Xie, X., Wang, P. and Zhang, M. (2019), "Reducing disturbance of crack location on crack depth-sensing tag", Sensor Review, 39(4), 449-455. https://doi.org/10.1108/sr-11-2018-0284   DOI
13 ATID (2017), All the Identification AT870N Guide for Customer; All the Identification, Seoul, Korea. https://channel.invengo.com/download/support/AT870N_WinCE_User-Guide-2017-05_Eng.pdf
14 AtlasRFIDstore (2019), Alien Short RFID White Wet Inlay, ALN-9662, Higgs; AtlasFRIDstore, Alabama, USA. http://www.atlasrfidstore.com/alien-short-rfid-white-wet-inlay-aln-9662-higgs-3/
15 Brownlee, J. (2018), How to Choose Loss Functions When Training Deep Learning Neural Networks; Machine Learning Mastery, San Juan, Puerto Rico. https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/
16 Giurgiutiu, V. and Zagrai, A. (2005), "Damage detection in thin plates and aerospace structures with the electro-mechanical impedance method", Struct. Health Monitor., 4(2), 99-118. https://doi.org/10.1177/1475921705049752   DOI
17 ASCE (2017), Report Card for America's Infrastructure; American Society of Civil Engineers; Reston, Washington, DC, USA. https://www.infrastructurereportcard.org/wp-content/uploads/2016/10/2017-Infrastructure-Report-Card.pdf
18 Zagrai, A.N. and Giurgiutiu, V. (2001), "Electro-Mechanical impedance method for crack detection in thin wall structures", In: The 3rd International Workshop on Structural Health Monitoring, Stanford University, CA, USA, September, pp. 77-86.
19 Helwig, N.E. (2017), "Multivariate linear regression", University of Minnesota, Minneapolis and Saint Paul, MN, USA. http://users.stat.umn.edu/~helwig/notes/mvlr-Notes.pdf
20 Vitols Associates (2020), O&G; Torrington, CT, USA. https://www.ogind.com/portfolio/uconn-north-campus-parking-garage
21 Fils, P.D. and Jang, S. (2020), "Wireless crack detection of a concrete building using low-cost RFID tags", In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems.
22 Bruciati, B., Jang, S. and Fils, P. (2019), "RFID-based crack detection of ultra high-performance concrete retrofitted beams", Sensors, 19(7), 1573. https://doi.org/10.3390/s19071573   DOI
23 Daniels, J. (2020), Ground Penetrating Radar; U.S. Environmental Protection Agency, Washington, DC, USA. https://clu-in.org/characterization/technologies/gpr.cfm
24 Elshafey, A.A., Haddara, M.R. and Marzouk, H. (2010), "Damage detection in offshore structures using neural networks", Marine Struct., 23(1), 131-145. https://doi.org/10.1016/j.marstruc.2010.01.005   DOI
25 Marindra, A.M.J. and Tian, G.Y. (2019), "Multiresonance chipless RFID sensor tag for metal defect characterization using principal component analysis", IEEE Sensors J., 19(18), art. 8718341, 8037-8046. https://doi.org/10.1109/jsen.2019.2917840   DOI
26 Giurgiutiu, V. and Craig, A.R. (1997), "Electro-mechanical (E/M) impedance method for structural health monitoring and non-destructive evaluation", International Workshop on Structural Health Monitoring, Stanford University, CA, USA, September.
27 Duchesne, J. and Fournier, B. (2013), "Deterioration of concrete by the oxidation of sulphide minerals in the aggregate", J. Civil Eng. Architect., 7(8), 922. https://doi.org/10.17265/1934-7359/2013.08.003   DOI
28 Gobi, M. and Ashe, B. (2019), "Final Report of the Special Commission to Study the Financial and Economic Impacts of Crumbling Concrete Foundations due to the Presence of Pyrrhotite", Special Commission, The General Court, Commonwealth of Massachusetts.
29 Keras (2015), Keras documentation: Losses; Mountain View, CA, USA. https://keras.io/api/losses/
30 Lienert, P. and Lee, J.L (2020), "Lidar laser-sensing technology: From self-driving cars to dance contests", Reuters, Las Vegas, NV, USA. https://www.reuters.com/article/us-tech-ces-lidar/lidar-laser-sensing-technology-from-self-driving-cars-to-dance-contests-idUSKBN1Z62AS
31 Martinez-Castro, R.E., Jang, S., Nicholas, J. and Bansal, R. (2017), "Experimental assessment of an RFID-based crack sensor for steel structures", Smart Mater. Struct., 26(8), art. 085035. https://doi.org/10.1088/1361-665x/aa7cd8   DOI
32 Medeiros, R.D., Ribeiro, M.L. and Tita, V. (2014), "Computational methodology of damage detection on composite cylinders: structural health monitoring for automotive components", Int. J. Automotive Compos., 1(1), 112. https://doi.org/10.1504/IJAUTOC.2014.064159   DOI
33 Raju, V. (1998), "Impedance-based health monitoring technique of composite reinforced structure." Proceedings of the 9th International Conference on Adaptive Structures and Technologies, Cambridge, MA, USA, October. https://ci.nii.ac.jp/naid/10029700000
34 Ruder, S. (2017), An overview of gradient descent optimization algorithms; Dublin, Ireland. https://arxiv.org/pdf/1609.04747.pdf
35 Kalansuriya, P., Bhattacharyya, R. and Sarma, S. (2013), "RFID tag antenna-based sensing for pervasive surface crack detection", IEEE Sensors J., 13(5), 1564-1570. https://doi.org/10.1109/jsen.2013.2240155   DOI
36 Schaefer, B. and Schaefer, J. (2020), "Crumbling Foundations", Schaefer Inspection Service, Inc., Woodbridge, CT, USA. https://mhschaefer.com/crumblingfoundations-2/
37 Sun, F.P., Liang, C. and Rogers, C.A. (1994), "Structural modal analysis using collocated piezoelectric actuator/sensors: an electromechanical approach", In: Smart Structures and Materials 1994: Smart Structures and Intelligent Systems, Vol. 2190, pp. 238-249. https://doi.org/10.1117/12.175186   DOI
38 Sepehry, N., Shamshirsaz, M. and Abdollahi, F. (2011), "Temperature variation effect compensation in impedance-based structural health monitoring using neural networks", J. Intel. Mat. Syst. Str., 20(10), 1-8. https://doi.org/10.1177/1045389X11421814   DOI
39 Sherpa, R., Fils, P. and Jang, S. (2021), "Crack detection of a reinforced concrete wall using low cost RFIDbased sensors", 2021 Transportation Research Board Annual Meeting, 1298 - Non-destructive Testing and Evaluation of Bridges, Washington DC, USA, January.
40 Sohn, H., Worden, K. and Farrar, C.R. (2002), "Statistical damage classification under changing environmental and operational conditions", J. Intell. Mater. Syst. Struct., 13(9), 561-574. https://doi.org/10.1106/104538902030904   DOI