Multi-sensor data collection and fusion using autoencoders in condition evaluation of concrete bridge decks
Tóm tắt
This paper presents a multi-sensor data collection and data fusion procedure for nondestructive evaluation/testing (NDE/NDT) of a concrete bridge deck. Three NDE technologies, vertical electrical impedance (VEI), ground-penetrating radar (GPR), and high-definition imaging (HDI) for surface crack detection, were deployed on the bridge deck. A neural network autoencoder was trained to quantify the relationship between VEI and GPR results using the data collected at common positions. This relationship was then used for fusion of VEI and GPR data to increase the reliability and spatial resolution of the NDE measurements and to generate a data-fused condition map that showed novel characteristics. Threshold values for VEI and GPR tests were obtained and used to determine the color scale in the fused map. Surface cracks identified from HDI show reasonable agreement with the deterioration areas on the data-fused condition map. Chloride concentration measurements on sound and deteriorated areas of the deck were consistent with the NDE results.
Tài liệu tham khảo
Gucunski N, Imani A, Romero F, Nazarian S, Yuan D, Wiggenhauser H, Shokouhi P, Taffe A, Kutrubes D (2013) Nondestructive testing to identify concrete bridge deck deterioration. Transportation Research Board SHRP 2 Report S2-R06A-RR-1.
Kohl C, Streicher D (2006) Results of reconstructed and fused NDT-data measured in the laboratory and on-site at bridges. Cem Concr Compos 28(4):402–413. https://doi.org/10.1016/j.cemconcomp.2006.02.005.
Kee S-H, Oh T, Popovics JS, Arndt RW, Zhu J (2012) Nondestructive bridge deck testing with air-coupled impact-echo and infrared thermography. J Bridg Eng 17(6):928–939. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000350.
Oh T, Kee S-H, Arndt RW, Popovics JS, Zhu J (2013) Comparison of NDT methods for assessment of a concrete bridge deck. J Eng Mech 139(3):305–314. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000441.
Islam MM, Kim J-M (2019) Vision-based autonomous crack detection of concrete structures using a fully convolutional encoder–decoder network. Sensors 19:4251. https://doi.org/10.3390/s19194251.
Hall DL, Llinas J (1997) An introduction to multisensor data fusion. Proc IEEE 85(1):6–23. https://doi.org/10.1109/5.554205.
Sbartaï Z-M, Breysse D, Larget M, Balayssac J-P (2012) Combining NDT techniques for improved evaluation of concrete properties. Cem Concr Compos 34(6):725–733. https://doi.org/10.1016/j.cemconcomp.2012.03.005.
Abu Dabous S, Yaghi S, Alkass S, Moselhi O (2017) Concrete bridge deck condition assessment using ir thermography and ground penetrating radar technologies. Autom Constr 81:340–354. https://doi.org/10.1016/j.autcon.2017.04.006.
Won K, Sim C (2020) Automated transverse crack mapping system with optical sensors and big data analytics. Sensors 20(7):1838. https://doi.org/10.3390/s20071838.
Guthrie WS, Baxter JS, Mazzeo BA (2018) Vertical electrical impedance testing of a concrete bridge deck using a rolling probe. NDT E Int 95:65–71. https://doi.org/10.1016/j.ndteint.2018.01.006.
Mazzeo BA, Guthrie WS (2019) Vertical electrical impedance scanner for concrete bridge deck assessment without direct rebar attachment. Transportation Research Board, NCHRP-IDEA Program Project Final Report No. 202. http://www.trb.org/Main/Blurbs/179622.aspx.
Baxter JS, Hendricks LJ, Guthrie WS, Mazzeo BA (2020) Instrumentation for multi-channel vertical electrical impedance scanning of concrete bridge decks. Eng Res Expr 2(3):035010.
Bartholomew PD, Guthrie WS, Mazzeo BA (2012) Vertical impedance measurements on concrete bridge decks for assessing susceptibility of reinforcing steel to corrosion. Rev Sci Instrum 83(8):085104. https://doi.org/10.1063/1.4740479.
Argyle HM (2014) Sensitivity of electrochemical impedance spectroscopy measurements to concrete bridge deck properties. Master’s thesis,Brigham Young University-Provo. http://hdl.lib.byu.edu/1877/etd6875.
Guthrie WS, Mazzeo BA (2015) Vertical impedance testing for assessing protection from chloride-based deicing salts provided by an asphalt overlay system on a concrete bridge deck In: 16th International Conference on Cold Regions Engineering, Salt Lake City, Utah, July 19–22, 2015, 358–369. https://doi.org/10.1061/9780784479315.032.
Baxter JS, Guthrie WS, Waters T, Barton JD, Mazzeo BA (2018) Vertical electrical impedance evaluation of asphalt overlays on concrete bridge decks In: AIP Conference Proceedings, 030011. https://doi.org/10.1063/1.5031534.
Barton J, Baxter J, Guthrie WS, Mazzeo BA (2019) Large-area electrode design for vertical electrical impedance scanning of concrete bridge decks. Rev Sci Instrum 90(2):025101. https://doi.org/10.1063/1.5058152.
Pashoutani S, Zhu J (2020) Ground penetrating radar data processing for concrete bridge deck evaluation. J Bridg Eng 25(7):04020030. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001566.
Pashoutani S, Zhu J (2019) Condition assessment of concrete bridge deck with asphalt overlay. Nebraska Department of Transportation Final Report SPR-P1(17) M065. https://trid.trb.org/view/1687979.
FroschFollow RJ, Blackman DT, Radabaugh RD (2003) Investigation of bridge deck cracking in various bridge superstructure systems. Indiana Department of Transportation Final Report FHWA/IN/JTRP-2002/25. https://doi.org/10.5703/1288284313257.
Ebrahimkhanlou A, Dubuc B, Salamone S (2019) A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels. Mech Syst Signal Process 130:248–272. https://doi.org/10.1016/j.ymssp.2019.04.050.
Hendricks LJ, Baxter JS, Chou Y, Thomas M, Boekweg E, Guthrie WS, Mazzeo BA (2020) High-speed acoustic impact-echo sounding of concrete bridge decks. J Nondestruct Eval 39(3):58.
Zabalza J, Ren J, Zheng J, Zhao H, Qing C, Yang Z, Du P, Marshall S (2016) Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185:1–10. https://doi.org/10.1016/j.neucom.2015.11.044.
Pathirage CSN, Li J, Li L, Hao H, Liu W, Ni P (2018) Structural damage identification based on autoencoder neural networks and deep learning. Eng Struct 172:13–28. https://doi.org/10.1016/j.engstruct.2018.05.109.
Gondara L (2016) Medical image denoising using convolutional denoising autoencoders In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 241–246. https://doi.org/10.1109/ICDMW.2016.0041.
Araki S, Hayashi T, Delcroix M, Fujimoto M, Takeda K, Nakatani T (2015) Exploring multi-channel features for denoising-autoencoder-based speech enhancement In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 116–120. https://doi.org/10.1109/ICASSP.2015.7177943.
Licciardi GA, Khan MM, Chanussot J, Montanvert A, Condat L, Jutten C (2012) Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction. EURASIP J Adv Signal Process, 207. https://doi.org/10.1186/1687-6180-2012-207.
Lai W-L, Kind T, Stoppel M, Wiggenhauser H (2013) Measurement of accelerated steel corrosion in concrete using ground-penetrating radar and a modified half-cell potential method. J Infrastruct Syst 19(2):205–220. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000083.
Dinh K, Zayed T, Moufti S, Shami A, Jabri A, Abouhamad M, Dawood T (2015) Clustering-Based Threshold Model for Condition Assessment of Concrete Bridge Decks with Ground-Penetrating Radar. Transp Res Rec 2522(1):81–89. https://doi.org/10.3141/2522-08.