Technical Papers
Dec 26, 2023

Automatic Rebar Picking for Corrosion Assessment of RC Bridge Decks with Ground-Penetrating Radar Data

Publication: Journal of Performance of Constructed Facilities
Volume 38, Issue 2

Abstract

The detection and evaluation of internal corrosion of concrete bridge decks is of critical importance for bridge structure safety. Ground penetrating radar (GPR) is a nondestructive testing technique that has been used widely for corrosion assessment of RC bridge decks. However, the recognition accuracy of traditional automatic data processing methods for blurred hyperbolic features is insufficient, which leads to a decrease in the accuracy of bridge deck corrosion assessment. To address this problem, this paper proposes an automatic rebar picking algorithm based on gradient information and a migration method for bridge deck corrosion assessment with GPR data. This method can be divided into three steps: thresholding processing based on B-scan gradient information, a limited hyperbolic summation-based recognition (LHSR) algorithm, and rebar localization. First, the GPR B-scan is transformed into its gradient image and thresholded to enhance hyperbolic features. The LHSR algorithm then is applied to the binarized gradient B-scan to identify the hyperbolas, locate the rebar, and extract the rebar reflection amplitude. Finally, the corrosion map of the bridge deck is generated based on the rebar position and the rebar reflection amplitude after depth-correction. A case study with GPR data from two tested bridges was employed to validate the feasibility of the proposed method. The results show that the precision and recall of automatic rebar picking by this method for poor-quality GPR data were 91.27% and 93.56%, which are significantly higher than those of the traditional methods. Moreover, the accuracy of the bridge deck corrosion map obtained by the proposed method also is significantly better than that of the traditional methods. It can be concluded that the proposed method can be used for rebar picking and corrosion assessment of RC bridge decks with GPR data.

Practical Applications

Ground penetrating radar is a nondestructive testing technology that has been used widely for corrosion assessment of concrete bridge decks. However, the traditional automatic processing method of GPR data has insufficient recognition accuracy for blurred features of rebars, resulting in a decrease in the accuracy of bridge deck corrosion assessment. To address this problem, this paper proposes an automatic method that can be used for feature identification and location of rebars. The corrosion map generated by the position and reflection amplitude of rebars could help engineers to conduct corrosion assessment and repair of concrete bridge decks. The feasibility of the proposed method was validated through a case study with GPR data form two actual bridges. Especially for poor-quality GPR data, the accuracy of rebar picking and corrosion maps of this method is significantly higher than that of traditional methods. It can be concluded that the proposed method can be used for rebar positioning and corrosion assessment of concrete bridge decks based on GPR inspection data.

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Data Availability Statement

All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This research work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 52250011, 51978128, and 51908104).

References

Abouhamad, M., T. Dawood, A. Jabri, M. Alsharqawi, and T. Zayed. 2017. “Corrosiveness mapping of bridge decks using image-based analysis of GPR data.” Autom. Constr. 80 (Aug): 104–117. https://doi.org/10.1016/j.autcon.2017.03.004.
Barnes, C. L., J.-F. Trottier, and D. Forgeron. 2008. “Improved concrete bridge deck evaluation using GPR by accounting for signal depth–amplitude effects.” NDT and E Int. 41 (6): 427–433. https://doi.org/10.1016/j.ndteint.2008.03.005.
Capineri, L., P. Grande, and J. A. G. Temple. 1998. “Advanced image-processing technique for real-time interpretation of ground-penetrating radar images.” Int. J. Imaging Syst. Technol. 9 (1): 51–59. https://doi.org/10.1002/(SICI)1098-1098(1998)9:1%3C51::AID-IMA7%3E3.0.CO;2-Q.
Choe, G., Y. Shinohara, G. Kim, S. Lee, E. Lee, and J. Nam. 2020. “Concrete corrosion cracking and transverse bar strain behavior in a reinforced concrete column under simulated marine conditions.” Appl. Sci. 10 (5): 1794. https://doi.org/10.3390/app10051794.
Cosenza, E., and D. Losanno. 2021. “Assessment of existing reinforced-concrete bridges under road-traffic loads according to the new Italian guidelines.” Struct. Concr. 22 (5): 2868–2881. https://doi.org/10.1002/suco.202100147.
Dinh, K., N. Gucunski, and T. H. Duong. 2018. “An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks.” Autom. Constr. 89 (May): 292–298. https://doi.org/10.1016/j.autcon.2018.02.017.
Dinh, K., N. Gucunski, J. Kim, and T. H. Duong. 2016. “Understanding depth-amplitude effects in assessment of GPR data from concrete bridge decks.” NDT E Int. 83 (10): 48–58. https://doi.org/10.1016/j.ndteint.2016.06.004.
Dinh, K., T. Zayed, F. Romero, and A. Tarussov. 2014. “Method for analyzing time-series GPR data of concrete bridge decks.” J. Bridge Eng. 20 (6): 04014086. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000679.
Dou, Q., L. Wei, D. R. Magee, and A. G. Cohn. 2017. “Real-time hyperbola recognition and fitting in GPR data.” IEEE Trans. Geosci. Remote Sens. 55 (1): 51–62. https://doi.org/10.1109/TGRS.2016.2592679.
Gohar, S., Y. Matsumoto, T. Maki, and S. Sakuma. 2023. “Investigation into vibration-based structural damage identification and amplitude-dependent damping ratio of reinforced concrete bridge deck slab under different loading states.” J. Civ. Struct. Heal. Monit. 13 (1): 133–148. https://doi.org/10.1007/s13349-022-00625-w.
Goulias, D. G., S. Cafiso, A. Di Graziano, S. G. Saremi, and V. Currao. 2020. “Condition assessment of bridge decks through ground-penetrating radar in bridge management systems.” J. Perform. Constr. Facil. 34 (5): 04020100. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001507.
Gucunski, N., S. Nazarian, A. Imani, and H. Azari. 2014. “Performance of NDT technologies in detection and characterization of reinforced concrete deck deterioration.” In Geo-Congress 2014 Technical Papers: Geo-Characterization and Modeling for Sustainability, 2436–2449. Reston, VA: ASCE. https://doi.org/10.1061/9780784413272.236.
Gucunski, N., B. Pailes, J. Kim, H. Azari, and K. Dinh. 2017. “Capture and quantification of deterioration progression in concrete bridge decks through periodical NDE surveys.” J. Infrastruct. Syst. 23 (1): B4016005. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000321.
Hou, F., W. Lei, S. Li, J. Xi, M. Xu, and J. Luo. 2021. “Improved Mask R-CNN with distance guided intersection over union for GPR signature detection and segmentation” Autom. Constr. 121 (Jan): 103414. https://doi.org/10.1016/j.autcon.2020.103414.
Kaur, P., K. J. Dana, F. A. Romero, and N. Gucunski. 2016. “Automated GPR rebar analysis for robotic bridge deck evaluation.” IEEE Trans. Cybern. 46 (10): 2265–2276. https://doi.org/10.1109/TCYB.2015.2474747.
Lai, W. W.-L., X. Derobert, and P. Annan. 2018. “A review of ground penetrating radar application in civil engineering: A 30-year journey from locating and testing to imaging and diagnosis.” NDT E Int. 96 (Jun): 58–78. https://doi.org/10.1016/j.ndteint.2017.04.002.
Lei, W., F. Hou, J. Xi, Q. Tan, M. Xu, X. Jiang, G. Liu, and Q. Gu. 2019. “Automatic hyperbola detection and fitting in GPR B-scan image.” Autom. Constr. 106 (Oct): 102839. https://doi.org/10.1016/j.autcon.2019.102839.
Li, S., H. Cai, D. M. Abraham, and P. Mao. 2014. “Estimating features of underground utilities: Hybrid GPR/GPS approach.” J. Comput. Civ. Eng. 30 (1): 04014108. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000443.
Li, S., X. Gu, X. Xu, D. Xu, T. Zhang, Z. Liu, and Q. Dong. 2021. “Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm.” Constr. Build. Mater. 273 (Mar): 121949. https://doi.org/10.1016/j.conbuildmat.2020.121949.
Li, Y., Z. Zhao, Y. Luo, and Z. Qiu. 2020. “Real-time pattern-recognition of GPR images with YOLO v3 implemented by Tensorflow.” Sensors 20 (22): 6476. https://doi.org/10.3390/s20226476.
Liu, H., C. Lin, J. Cui, L. Fan, X. Xie, and B. F. Spencer. 2020. “Detection and localization of rebar in concrete by deep learning using ground-penetrating radar.” Autom. Constr. 118 (Oct): 103279. https://doi.org/10.1016/j.autcon.2020.103279.
Maas, C., and J. Schmalzl. 2013. “Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar.” Comput. Geosci. 58 (Aug): 116–125. https://doi.org/10.1016/j.cageo.2013.04.012.
Martino, N., K. Maser, R. Birken, and M. Wang. 2015. “Quantifying bridge deck corrosion using ground-penetrating radar.” Res. Nondestr. Eval. 27 (May): 112–124. https://doi.org/10.1080/09349847.2015.1067342.
Mertens, L., R. Persico, L. Matera, and S. Lambot. 2016. “Automated detection of reflection hyperbolas in complex GPR images with no a priori knowledge on the medium.” IEEE Trans. Geosci. Remote Sens. 54 (1): 580–596. https://doi.org/10.1109/TGRS.2015.2462727.
Miluccio, G., D. Losanno, F. Parisi, and E. Cosenza. 2021. “Traffic-load fragility models for prestressed concrete girder decks of existing Italian highway bridges.” Eng. Struct. 249 (Dec): 113367. https://doi.org/10.1016/j.engstruct.2021.113367.
Moselhi, O., M. Ahmed, and A. Bhowmick. 2017. “Multisensor data fusion for bridge condition assessment.” J. Perform. Constr. Facil. 31 (4): 04017008. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001000.
Pashoutani, S., and J. Zhu. 2020. “Ground penetrating radar data processing for concrete bridge deck evaluation.” J. Bridge Eng. 25 (7): 04020030. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001566.
Puente, I., M. Solla, H. González-Jorge, and P. Arias. 2015. “NDT documentation and evaluation of the Roman Bridge of Lugo using GPR and mobile and static LiDAR.” J. Perform. Constr. Facil. 29 (1): 06014004. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000531.
Reichman, D., L. M. Collins, and J. M. Malof. 2017. “Some good practices for applying convolutional neural networks to buried threat detection in ground-penetrating radar.” In Proc., 9th Int. Workshop on Advanced Ground-penetrating Radar, 1–5. New York: IEEE. https://doi.org/10.1109/IWAGPR.2017.7996100.
Saleem, M. A., M. N. Zafar, M. M. Saleem, and J. Xia. 2021. “Recent developments in the prefabricated bridge deck systems.” Case Stud. Constr. Mater. 15 (Dec): e00750. https://doi.org/10.1016/j.cscm.2021.e00750.
Sun, H., S. Pashoutani, and J. Zhu. 2018. “Nondestructive evaluation of concrete bridge decks with automated acoustic scanning system and ground-penetrating radar.” Sensors 18 (6): 1955. https://doi.org/10.3390/s18061955.
Tamhane, D., S. Banerjee, and S. Tallur. 2022. “Monitoring corrosion in sacrificial anodes with pulsed eddy current and electromechanical impedance: A comparative analysis.” IEEE Sens. J. 22 (8): 8147–8154. https://doi.org/10.1109/JSEN.2022.3157646.
Tong, Z., D. Yuan, J. Gao, Y. Wei, and H. Dou. 2020. “Pavement-distress detection using ground-penetrating radar and network in networks.” Constr. Build. Mater. 233 (Feb): 117352. https://doi.org/10.1016/j.conbuildmat.2019.117352.
Wiwatrojanagul, P., R. Sahamitmongkol, S. Tangtermsirikul, and N. Khamsemanan. 2017. “A new method to determine locations of rebars and estimate cover thickness of RC structures using GPR data.” Constr. Build. Mater. 140 (Jun): 257–273. https://doi.org/10.1016/j.conbuildmat.2017.02.126.
Xiang, Z., G. Ou, and A. Rashidi. 2020. “Integrated approach to simultaneously determine 3D location and size of rebar in GPR data.” J. Perform. Constr. Facil. 34 (5): 04020097. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001502.
Xiang, Z., A. Rashidi, and G. Ou. 2019. “States of practice and research on applying GPR technology for labeling and scanning constructed facilities.” J. Perform. Constr. Facil. 33 (5): 03119001. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001313.
Yelf, R. 2004. “Where is true time zero?” In Proc., 10th Int. Conf. on Ground Penetrating Radar, GPR 2004, 279–282. Delft, Netherlands: IEEE.
Yuan, C., S. Li, H. Cai, and V. R. Kamat. 2018. “GPR signature detection and decomposition for mapping buried utilities with complex spatial configuration.” J. Comput. Civ. Eng. 32 (4): 04018026. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000764.
Zhang, J., X. Yang, W. Li, S. Zhang, and Y. Jia. 2020. “Automatic detection of moisture damages in asphalt pavements from GPR data with deep CNN and IRS method.” Autom. Constr. 113 (May): 103119. https://doi.org/10.1016/j.autcon.2020.103119.
Zhang, Y. C., T. H. Yi, S. B. Lin, H. N. Li, and S. T. Lv. 2022. “Automatic corrosive environment detection of RC bridge decks from ground-penetrating radar data based on deep learning.” J. Perform. Constr. Facil. 36 (2): 04022011. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001712.
Zhou, F., Z. Chen, H. Liu, J. Cui, B. Spencer, and G. Fang. 2018a. “Simultaneous estimation of rebar diameter and cover thickness by a GPR-EMI dual sensor.” Sensors 18 (9): 2969. https://doi.org/10.3390/s18092969.
Zhou, X., H. Chen, and J. Li. 2018b. “An automatic GPR B-scan image interpreting model.” IEEE Trans. Geosci. Remote Sens. 56 (6): 3398–3412. https://doi.org/10.1109/TGRS.2018.2799586.

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Go to Journal of Performance of Constructed Facilities
Journal of Performance of Constructed Facilities
Volume 38Issue 2April 2024

History

Received: Jun 8, 2023
Accepted: Oct 26, 2023
Published online: Dec 26, 2023
Published in print: Apr 1, 2024
Discussion open until: May 26, 2024

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Yu-Chen Zhang, S.M.ASCE [email protected]
Ph.D. Candidate, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China. Email: [email protected]
Yan-Liang Du, Ph.D. [email protected]
Professor, College of Civil and Transportation Engineering, Shenzhen Univ., Shenzhen 518061, China. Email: [email protected]
Ting-Hua Yi, M.ASCE [email protected]
Professor, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China (corresponding author). Email: [email protected]
Song-Han Zhang, Ph.D. [email protected]
Associate Professor, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China. Email: [email protected]

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  • Automatic Correction of Abnormal Ground Penetrating Radar Data for Concrete Bridge Deck Corrosion Assessment, Journal of Performance of Constructed Facilities, 10.1061/JPCFEV.CFENG-4719, 38, 5, (2024).

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