Automatic Correction of Abnormal Ground Penetrating Radar Data for Concrete Bridge Deck Corrosion Assessment
Publication: Journal of Performance of Constructed Facilities
Volume 38, Issue 5
Abstract
Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for the detection and assessment of internal corrosion in concrete bridge decks. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper analyzes some common abnormal data from actual bridges GPR data and proposes corresponding automatic algorithms for anomaly correction to enhance assessment accuracy. The automatic algorithm focuses on two main aspects: correcting anomalies in direct coupling wave amplitudes based on data statistics and mitigating the impact of abnormal data due to incorrectly picked rebar on depth correction using density clustering. The specific process of the automatic method can be divided into four steps. First, automatic rebar picking is performed based on the preprocessed GPR data. Next, data statistics analysis is implemented on the extracted rebar data to identify and correct abnormal amplitude data. Then, the true rebar data are identified for depth correction based on density clustering. Finally, the bridge deck corrosion map is generated based on the corrected rebar reflection amplitudes and rebar positions. The feasibility of this method was verified through a case study with GPR data from two in-service bridges. The results show that this method can effectively and automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by the proposed method is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR.
Practical Applications
Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for concrete bridge deck corrosion detection and assessment. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper proposes a set of automatic data processing procedures for anomaly correction to improve the corrosion assessment accuracy. The feasibility of the proposed algorithms was validated through a case study with GPR data from two in-service bridges. The results show that these algorithms can effectively automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by these algorithms is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR.
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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 and 12002224).
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.
Alsharqawi, M., T. Zayed, and A. Shami. 2020. “Ground penetrating radar-based deterioration assessment of RC bridge decks.” Constr. Innovation 20 (1): 1–17. https://doi.org/10.1108/CI-08-2019-0076.
ASTM. 2008. Standard test method for evaluating asphalt-covered concrete bridge decks using ground penetrating radar. ASTM D6087-08. West Conshohocken, PA: ASTM.
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 & E Int. 41 (6): 427–433. https://doi.org/10.1016/j.ndteint.2008.03.005.
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.
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., and T. Zayed. 2015. “GPR-based fuzzy model for bridge deck corrosiveness index.” J. Perform. Constr. Facil. 30 (4): 04015069. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000815.
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.
Du, Y. L., T. H. Yi, X. J. Li, X. L. Rong, L. J. Dong, D. W. Wang, G. Yang, and Z. Leng. 2023. “Advances in intellectualization of transportation infrastructures.” Engineering 23 (4): 64. https://doi.org/10.1016/j.eng.2023.01.011.
Faris, N., T. Zayed, E. M. Abdelkader, and A. Fares. 2023. “Corrosion assessment using ground penetrating radar in reinforced concrete structures: Influential factors and analysis methods.” Autom. Constr. 156 (Dec): 105130. https://doi.org/10.1016/j.autcon.2023.105130.
Goulias, D. G., S. Cafiso, A. Di Graziano, and S. G. Saremi. 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, Geotechnical Special Publication 234, edited by M. Abu-Farsakh, X. Yu, and L. R. Hoyos, 2436–2449. Reston, VA: ASCE. https://doi.org/10.1061/9780784413272.236.
Gucunski, N., B. Pailes, J. Kim, H. Azari, and K. Dinh. 2016. “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.
Hasan, I., and N. Yazdani. 2016. “An experimental study for quantitative estimation of rebar corrosion in concrete using ground-penetrating radar.” J. Eng. 2016 (Jan): 1–8. https://doi.org/10.1155/2016/8536850.
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.
Li, Y., Z. Zhao, Y. Luo, and Z. Qiu. 2020. “Real-time pattern-recognition of GPR images with YOLOv3 implemented by tensorflow.” Sensors 20 (22): 6476. https://doi.org/10.3390/s20226476.
Liu, H., C. Lina, J. Cuia, L. Fanb, X. Xie, and B. F. Spencerd. 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.
Martino, N., K. Maser, R. Birken, and M. Wang. 2015. “Quantifying bridge deck corrosion using ground-penetrating radar.” Res. Nondestr. Eval. 27 (2): 112–124. https://doi.org/10.1080/09349847.2015.1067342.
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. 2013. “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.
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.
Tarussov, A., M. Vandry, and A. De La Haza. 2013. “Condition assessment of concrete structures using a new analysis method: Ground-penetrating radar computer-assisted visual interpretation.” Constr. Build. Mater. 38 (1): 1246–1254. https://doi.org/10.1016/j.conbuildmat.2012.05.026.
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.
Yang, D. H., H. L. Gu, T. H. Yi, and H. N. Li. 2023. “Bridge cable anomaly detection based on local variability in feature vector of monitoring group cable forces.” J. Bridge Eng. 28 (6): 04023030. https://doi.org/10.1061/JBENF2.BEENG-6084.
Ye, X. J., P. R. Wu, A. R. Liu, X. Y. Zhan, Z. Y. Wang, and Y. H. Zhao. 2023. “A deep learning-based method for automatic abnormal data detection: Case study for bridge structural health monitoring.” Int. J. Struct. Stab. Dyn. 23 (11): 2350131. https://doi.org/10.1142/S0219455423501316.
Yelf, R. 2004. “Where is true time zero?” In Proc., 10th Int. Conf. on Ground Penetrating Radar, GPR 2004, 279–282. New York: IEEE.
Yu, Y., D. Zhu, J. D. Wang, and Y. Zhao. 2017. “Abnormal data detection for multivariate alarm systems based on correlation directions.” J. Loss Prev. Process Ind. 45 (Jan): 43–55. https://doi.org/10.1016/j.jlp.2016.11.011.
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, Y. C., Y. L. Du, T. H. Yi, and S. H. Zhang. 2023. “Automatic rebar picking for corrosion assessment of RC bridge decks with ground-penetrating radar data.” J. Perform. Constr. Facil. 38 (2): 04023069. https://doi.org/10.1061/JPCFEV.CFENG-4591.
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.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 38 • Issue 5 • October 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Nov 14, 2023
Accepted: Apr 17, 2024
Published online: Jul 8, 2024
Published in print: Oct 1, 2024
Discussion open until: Dec 8, 2024
ASCE Technical Topics:
- Analysis (by type)
- Bridge components
- Bridge decks
- Bridge engineering
- Bridge management
- Bridge tests
- Bridges
- Bridges (by material)
- Communication systems
- Concrete bridges
- Corrosion
- Data analysis
- Decks
- Deterioration
- Engineering fundamentals
- Engineering materials (by type)
- Field tests
- Infrastructure
- Lifeline systems
- Materials characterization
- Materials engineering
- Metals (material)
- Methodology (by type)
- Reinforcing steel
- Research methods (by type)
- Steel
- Structural engineering
- Structural systems
- Tests (by type)
Authors
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