Multisensor Data Fusion for Bridge Condition Assessment
Publication: Journal of Performance of Constructed Facilities
Volume 31, Issue 4
Abstract
Considerable work has been carried out using a number of sensing technologies for condition assessment of civil infrastructure. Fewer efforts, however, have been directed for integrating the use of these technologies. This paper presents a newly developed method for automated condition assessment and rating of concrete bridge decks. The method integrates the use of ground-penetrating radar (GPR) and infrared thermography (IR) technologies. It utilizes a data-fusion technique to improve the accuracy of detecting defects and, accordingly, that of condition assessment. In this research, pixel-level image fusion is applied to assess bridge condition using these two technologies. GPR data are displayed as three-dimensional (3D) from 24 scans equally spaced by 0.33 m to interpret a section of a bridge deck surface in Montréal, Canada. The analysis of reflected amplitude waves is used to detect subsurface changed conditions. The GPR data are fused with infrared thermography images using a wavelet transform technique. Four scenarios based on image processing are studied and their application before and after data fusion is assessed in relation to accuracy of the employed fusion process. The concrete deck’s condition is assessed based on deteriorated areas that are extracted from the new fused image. Analysis of the results showed that bridge condition assessment can be improved with image fusion and this can enhance the capabilities of inspectors in interpretation of the results obtained.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors would like to thank Dr. T. Zayed of Concordia University for sharing the GPR data and enabling access to RADAN 7 software used in this study. They also would like to acknowledge the financial support provided by NSERC for this research.
References
FLIR [Computer software]. FLIR System, Inc., Wilsonville, OR.
MATLAB [Computer software]. MathWorks, Natick, MA.
Radan [Computer software]. Geophysical Survey Systems, Inc., Nashua, NH.
Abdel-Qader, I., Yohali, S., Abudayyeh, O., and Yehia, S. (2008). “Segmentation of thermal images for non-destructive evaluation of bridge decks.” NDT & E Int., 41(5), 395–405.
Adhikari, R., Moselhi, O., and Bagchi, A. (2014). “Image-based retrieval of concrete crack properties for bridge inspection.” J. Autom. Constr., 39(1), 180–194.
Adhikari, R. S., Moselhi, O., and Bagchi, A. (2013). “A study of image-based element condition index for bridge inspection.” Proc., 30th Int. Symp. on Automation and Robotics in Construction, International Association for Automation & Robotics in Construction (IAARC), U.K., 345–356.
ASCE. (2013). “Infrastructure report card.” Reston, VA.
Balaguer, C., Montero, R., Victores, J. G., Martínez, S., and Jardón, A. (2014). “Towards fully automated tunnel inspection: A survey and future trends.” Proc., 31st Int. Symp. on Automation and Robotics in Construction and Mining, N.S.W. Univ. of Technology, Sydney, 19–33.
Barnes, C. L., Trottier, J. F., and Forgeron, D. (2008). “Improved concrete bridge deck evaluation using GPR by accounting for signal depth-amplitude effects.” NDT & E Int., 41(6), 427–433.
Carvalho, R. N., Laskey, K. B., Costa, P. C., Ladeira, M., Santos, L. L., and Matsumoto, S. (2009). “Probabilistic ontology and knowledge fusion for procurement fraud detection in Brazil.” Proc., 5th Int. Conf. on Uncertainty Reasoning for the Semantic Web-Volume (URSW 527), CEUR-WS. org, 3–14.
Chong, K. P., Carino, N. J., and Washer, G. (2003). “Health monitoring of civil infrastructures.” J. Smart Mater. Struct., 12(3), 483–493.
Clark, M. R., McCann, D. M., and Forde, M. C. (2003). “Application of infrared thermography to the non-destructive testing of concrete and masonry bridges.” NDT & E Int., 36(4), 265–275.
Dinh, K., Zayed, T., Romero, F., and Tarussov, A. (2014). “Method for analyzing time-series GPR data of concrete bridge decks.” J. Bridge Eng., .
Dong, X., et al. (2014). “From data fusion to knowledge fusion.” Proc., Very Large Database Endowment (VLDB) Endowment, 7(10), 881–892.
Gucunski, N., and Nazarian, S. (2010). “Material characterization and condition assessment of reinforced concrete bridge decks by complementary NDE technologies.” Structures Congress, ASCE, Reston, VA, 429–439.
Gucunski, N., Yan, M., Wang, Z., Fang, T., and Maher, A. (2012). “Rapid bridge deck condition assessment using three-dimensional visualization of impact echo data.” J. Infrastruct. Syst., 12–24.
Hall, D. L., and Llinas, J. (1997). “An introduction to multi sensor data fusion.” Proc., IEEE, 85(1), 6–23.
Hoseini, S., and Ashraf, M. (2013). “Computational complexity comparison of multi-sensor single target data fusion methods by MATLAB.” Int. J. Chaos Control Modell. Simul., 2(2), 1–8.
Huang, Y., Xiang, Q., Lu, J., and Leng, H. (2010). “Application of data fusion technology in freeway infrastructure safety assessment.” Proc., Int. Conf. of Logistics Engineering and Management, ASCE, Reston, VA, 2340–2345.
Laa, H. M., Gucunski, N., Kee, S. H., and Nguyen, L. (2014). “Visual and acoustic data analysis for the bridge deck inspection robotic system.” Proc., 31st Int. Symp. on Automation and Robotics in Construction and Mining, Sydney, Australia.
Maierhofer, C., et al. (2004). “Complementary application of radar, impact-echo, and ultrasonics for testing concrete structures and metallic tendon ducts.” J. Transp. Res. Board, 1892, 170–177.
Mallat, S. G. (1989). “A theory for multiresolution signal decomposition: The wavelet representation.” IEEE Trans. Pattern Anal. Mach. Intell., 11(7), 674–693.
Maser, K. R. (1996). “Condition assessment of transportation infrastructure using ground-penetrating radar.” J. Infrastruct. Syst., 94–101.
Matsumoto, M., Mitani, K., Sugimoto, M., Hashimoto, K., and Miller, R. (2012). “Innovative bridge assessment methods using image processing and infrared thermography technology.” Int. Assoc. Bridge Struct. Eng., 18(13), 1181–1188.
Minnesota Department of Transportation. (2013). “Bridge inspection field manual.” MnDOT Bridge Office, Oakdale, MN.
Moselhi, O., and Shehab-Eldeen, T. (1999). “Automated detection of surface defects in water and sewer pipes.” Autom. Constr., 8(5), 581–588.
Moselhi, O., and Shehab-Eldeen, T. (2000). “Classification of defects in sewer pipes using neural networks.” J. Infrastruct. Syst., 97–104.
Naidu, V., and Raol, J. (2008). “Pixel-level image fusion using wavelets and principal component analysis.” Defence Sci. J., 58(3), 338–352.
Olson, L. D., Tinkey, Y., and Miller, P. (2011). “Concrete bridge condition assessment with impact echo scanning” GeoHunan Int. Conf. on Emerging Technologies for Material, Design, Rehabilitation, and Inspection of Roadway Pavements, ASCE, Reston, VA, 59–66.
Pajares, G., and Manuel de la Cruz, J. (2004). “A wavelet-based image fusion tutorial.” Pattern Recognit., 37(9), 1855–1872.
Parrillo, R., Roberts, R., and Haggan, A. (2006). “Bridge deck condition assessment using ground penetrating radar.” Proc., European Conf. for Non-Destructive Testing, YXLON International GmbH, Hamburg, Germany, 1–12.
Ryan, Ø. (2004). “Applications of the wavelet transform in image processing.” Norwegian Research Council, Oslo, Norway.
Shahandashti, S., et al. (2011). “Data-fusion approaches and applications for construction engineering.” J. Constr. Eng. Manage., 863–869.
Shahi, A., Safa, M., Haas, C., and West, J. (2014). “Data fusion process management for automated construction progress estimation.” J. Comput. Civ. Eng., 04014098.
Shin, H., and Grivas, D. (2003). “How accurate is ground-penetrating radar for bridge deck condition assessment?” J. Transp. Res. Board, 1845(1), 139–147.
Simone, G., Farina, A., Morabito, F., Serpico, S., and Bruzzone, L. (2002). “Image fusion techniques for remote sensing applications.” J. Inform. Fusion, 3(1), 3–15.
Statistics Canada. (2009) “Age of public infrastructure: A provincial perspective.” ⟨http://www.statcan.gc.ca/pub/11-621-m/11-621-m2008067-eng.htm#t1⟩ (Nov. 12, 2009).
Tawhed, W. F., and Gassman, S. L. (2002). “Damage assessment of concrete bridge decks using impact-echo method.” Mater. J., 99(3), 273–281.
Toufik, B., and Mokhtar, N. (2012). “The wavelet transform for image processing applications.” Advances in wavelet theory and their applications in engineering, D. Baleanu, ed., INTECH, Rijeka, Croatia, 395–422.
Walker, J. (2006). “Wavelet-based image processing.” Appl. Anal., 85(4), 439–458.
Wang, R., Zhou, H., Dong, G., and Zhu, C. (2010). “Image fusion for high-resolution data in MATLAB.” Int. Conf. Computer Application and System Modeling, IEEE, Taiyuan, China, 153–156.
Xia, P. Q., and Brownjohn, J. M. W. (2004). “Bridge structural condition assessment using systematically validated finite-element model.” J. Bridge Eng., 418–423.
Yaghi, S. (2014). “Integrated remote sensing technologies for condition assessment of concrete bridges.” Ph.D. dissertation, Dept. of Building, Civil and Environmental Engineering, Concordia Univ., Montréal.
Yehia, S., Abudayyeh, O., Nabulsi, S., and Abdelqader, I. (2007). “Detection of common defects in concrete bridge decks using nondestructive evaluation techniques.” J. Bridge Eng., 215–225.
Zhang, Y., and Ji, Q. (2006). “Active and dynamic information fusion for multi sensor systems with dynamic bayesian networks.” IEEE Trans. Syst. Man Cybern., 36(2), 467–472.
Zhang, Y., Wei, X., Tsai, Y. T., Zhu, J., Fetrat, F. A., and Gucunski, N. (2012). “Multisensor data fusion for impact-echo testing of concrete structures.” J. Smart Mater. Struct., 21(7), 1–7.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 31 • Issue 4 • August 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Dec 1, 2015
Accepted: Oct 18, 2016
Published ahead of print: Feb 6, 2017
Published online: Feb 7, 2017
Discussion open until: Jul 7, 2017
Published in print: Aug 1, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.