Integrated Condition-Based Rating Model for Sustainable Bridge Management
Publication: Journal of Performance of Constructed Facilities
Volume 34, Issue 5
Abstract
In North America, common practices in bridge condition assessment include visual inspection and nondestructive evaluation (NDE) techniques, and results are reported as condition ratings of the bridge components. Assigning a specific condition rating to the component is a difficult task, especially when the threshold values defining the borderlines between the different ratings are not specified. These thresholds are subjectively assigned based on the judgment and experience of the inspector or expert, and can influence decisions on maintenance, repair, and replacement (MRR) of bridges and impact their safety and serviceability. Quality inspection data and accurate condition assessment and rating are the basis for determining appropriate MRR decisions. Thus, in this paper, a novel quality function deployment (QFD)-based approach for assessing bridges is proposed to develop an integrated condition rating based on data collected from visual inspection and ground penetrating radar (GPR) technology, while identifying clear thresholds between the different ratings. The -means clustering technique, used to define the rating thresholds, is one of the unsupervised learning algorithms that solves the subjective determination of the threshold values problem. This work used four case studies on bridges in the Province of Quebec. The integrated condition model produced ratings of 0.48, 0.49, 0.37, and 0.15 for the four case studies. The developed rating model represented by an integrated condition index was validated with an average validity percentage greater than 81%. The proposed method is expected to advance the state of the art for bridge condition assessment and rating by providing an objective means for making proper MRR decisions.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
References
Abdul Rahman, M., M. Alsharqawi, and T. Zayed. 2018. “A review on defects detection in RC bridge decks with a model developed for automated corrosion detection using GPR.” In Proc., 10th Int. Conf. on Short and Medium Span Bridges (No. 194). Quebec: Canadian Society for Civil Engineering.
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.
Abu Dabous, S., and S. Alkass. 2010. “A stochastic method for condition rating of concrete bridges.” In Proc., Construction Research Congress 2010: Innovation for Reshaping Construction Practice, 558–567. Reston, VA: ASCE.
Adhikari, R., O. Moselhi, and A. Bagchi. 2014. “Image-based retrieval of concrete crack properties for bridge inspection.” Autom. Constr. 39 (Apr): 180–194. https://doi.org/10.1016/j.autcon.2013.06.011.
Alampalli, S. 2010. “Special issue on bridge inspection and evaluation.” J. Bridge Eng. 15 (4): 349–351. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000129.
Alsharqawi, M., S. Abu Dabous, and T. Zayed. 2016. “Quality function deployment based method for condition assessment of concrete bridges.” In Proc., Transportation Research Board 95th Annual Meeting (No.16-4431). Washington, DC: Transportation Research Board.
Alsharqawi, M., T. Zayed, and S. Abu Dabous. 2017. “Common practices in assessing conditions of concrete bridges.” In Vol. 120 of Proc., MATEC Web of Conf., 02016. Les Ulis, France: EDP Sciences.
Alsharqawi, M., T. Zayed, and S. Abu Dabous. 2018. “Integrated condition rating and forecasting method of bridge decks using visual inspection and ground penetrating radar.” Autom. Constr. 89 (May): 135–145. https://doi.org/10.1016/j.autcon.2018.01.016.
Alsharqawi, M., T. Zayed, and A. Shami. 2020. “Ground penetrating radar-based deterioration assessment of RC bridge decks.” Constr. Innov. 20 (1): 1–17. https://doi.org/10.1108/CI-08-2019-0076.
American Railway Engineering Association. 1990. Manual for railway engineering. Washington, DC: American Railway Engineering Association.
Bolar, A., S. Tesfamariam, and R. Sadiq. 2013. “Condition assessment for bridges: A hierarchical evidential reasoning (HER) framework.” Struct. Infrastruct. Eng. 9 (7): 648–666. https://doi.org/10.1080/15732479.2011.602979.
Cattan, J., and J. Mohammadi. 1997. “Analysis of bridge condition rating data using neural networks.” Comput. Aided Civ. Infrastruct. Eng. 12 (6): 419–429. https://doi.org/10.1111/0885-9507.00074.
Chase, S., Y. Adu-Gyamfi, and P. Tunuguntla. 2015. “Bridge deck sub-surface defect detection using time-lapse thermography.” In Proc., Transportation Research Board 94th Annual Meeting, 15. Washington, DC: Transportation Research Board of the National Academies.
Chen, M. 2009. “A fuzzy evaluation approach for bridge based on domain knowledge.” In Proc., Int. Asia Conf. on Informatics in Control, Automation and Robotics, 269–272. New York: IEEE.
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.
Duda, R., P. Hart, and D. Stork. 2012. Pattern classification. New York: Wiley.
FHWA (Federal Highway Administration). 1979. Bridge inspector’s training manual. Washington, DC: USDOT.
FHWA (Federal Highway Administration). 2005. Recording and coding guide for the structure inventory and appraisal of the nation’s bridges. Washington, DC: USDOT.
Fisher, D. 1987. “Knowledge acquisition via incremental conceptual clustering.” Mach. Learn. 2 (2): 139–172. https://doi.org/10.1007/BF00114265.
Gucunski, N., F. Romero, S. Kruschwitz, R. Feldmann, and H. Parvardeh. 2011. Comprehensive bridge deck deterioration mapping of nine bridges by nondestructive evaluation technologies.. Ames, IA: Iowa Highway Research Board.
Hammad, A., J. Yan, and B. Mostofi. 2007. “Recent development of bridge management systems in Canada.” In Proc., Annual Conf. and Exhibition of the Transportation Association of Canada: Transportation—An Economic Enabler, 20. Saskatchewan, Canada: Transportation Association of Canada.
Hiasa, S., F. Necati Catbas, M. Matsumoto, and K. Mitani. 2017. “Considerations and issues in the utilization of infrared thermography for concrete bridge inspection at normal driving speeds.” J. Bridge Eng. 22 (11): 04017101. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001124.
Huang, Z. 1998. “Extensions to the k-means algorithm for clustering large data sets with categorical values.” Data Min. Knowl. Discovery 2 (3): 283–304. https://doi.org/10.1023/A:1009769707641.
Jain, A. 2010. “Data clustering: 50 years beyond k-means.” Pattern Recognit. Lett. 31 (8): 651–666. https://doi.org/10.1016/j.patrec.2009.09.011.
Jain, K., and B. Bhattacharjee. 2011. “Application of fuzzy concepts to the visual assessment of deteriorating reinforced concrete structures.” J. Constr. Eng. Manage. 138 (3): 399–408. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000430.
Kanungo, T., D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu. 2002. “An efficient k-means clustering algorithm: Analysis and implementation.” IEEE Trans. Pattern Anal. Mach. Intell. 24 (7): 881–892. https://doi.org/10.1109/TPAMI.2002.1017616.
Kee, S., and N. Gucunski. 2016. “Interpretation of flexural vibration modes from impact-echo testing.” J. Infrastruct. Syst. 22 (3): 04016009. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000291.
Li, M., N. Anderson, L. Sneed, and N. Maerz. 2016. “Application of ultrasonic surface wave techniques for concrete bridge deck condition assessment.” J. Appl. Geophys. 126 (Mar): 148–157. https://doi.org/10.1016/j.jappgeo.2016.01.020.
Madanat, S., R. Mishalani, and W. Ibrahim. 1995. “Estimation of infrastructure transition probabilities from condition rating data.” J. Infrastruct. Syst. 1 (2): 120–125. https://doi.org/10.1061/(ASCE)1076-0342(1995)1:2(120).
Maser, K. 2005. “Automated systems for infrastructure condition assessment.” J. Infrastruct. Syst. 11 (3): 153. https://doi.org/10.1061/(ASCE)1076-0342(2005)11:3(153).
Melhem, H., and S. Aturaliya. 1996. “Bridge condition rating using an eigenvector of priority settings.” Comput. Aided Civ. Infrastruct. Eng. 11 (6): 421–432. https://doi.org/10.1111/j.1467-8667.1996.tb00354.x.
Mierswa, I., M. Wurst, R. Klinkenberg, M. Scholz, and T. Euler. 2006. “Yale: Rapid prototyping for complex data mining tasks.” In Proc., 12th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 935–940. New York: Association for Computing Machinery.
Mohammed Abdelkader, E., M. Marzouk, and T. Zayed. 2020. “A self-adaptive exhaustive search optimization-based method for restoration of bridge defects images.” Int. J. Mach. Learn. Cybern. 1–58. https://doi.org/10.1007/s13042-020-01066-x.
Moufti, S., T. Zayed, and S. Abu Dabous. 2014. “Defect based condition assessment of concrete bridges: A fuzzy hierarchical evidential reasoning approach.” Transp. Res. Rec. 2431 (1): 88–96. https://doi.org/10.3141/2431-12.
MTQ (Quebec Ministry of Transportation). 2012. Manuel d’inspection des structures. Montreal, QC, Canada: Ministère des Transports.
Nguyen, T. D., K. T. Tran, and N. Gucunski. 2017. “Detection of bridge-deck delamination using full ultrasonic waveform tomography.” J. Infrastruct. Syst. 23 (2): 04016027. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000323.
Omar, T., and M. Nehdi. 2016. “Application of passive infrared thermography for the detection of defects in concrete bridge elements.” In Proc., Annual Conference and Exhibition of the Transportation Association of Canada. Toronto, ON, Canada: Transportation Association of Canada.
Ramanjaneyulu, K., S. Gopalakrishnan, N. Lakshmanan, and S. Sasmal. 2006. “Fuzzy logic based condition rating of existing reinforced concrete bridges.” J. Perform. Constr. Facil. 20 (3): 261–273. https://doi.org/10.1061/(ASCE)0887-3828(2006)20:3(261).
Sasmal, S., K. Ramanjaneyulu, and N. Iyer. 2012. “Condition ranking and rating of bridges using fuzzy logic.” In Fuzzy logic-emerging technologies and applications. Rijeka, Croatia: InTech.
Sung, K., and T. Poggio. 1998. “Example-based learning for view-based human face detection.” IEEE Trans. Pattern Anal. Mach. Intell. 20 (1): 39–51. https://doi.org/10.1109/34.655648.
Thompson, K., and P. Langley. 1991. “Concept formation in structured domains.” In Concept formation: Knowledge and experience in unsupervised learning, 127–161. San Mateo, CA: Elsevier.
Wagstaff, K., C. Cardie, S. Rogers, and S. Schrödl. 2001. “Constrained k-means clustering with background knowledge.” In ICML ’01: Proc., 18th Int. Conf. on Machine Learning, 577–584. New York: Association for Computing Machinery.
Washer, G., N. Bolleni, and R. Fenwick. 2010. “Thermographic imaging of subsurface deterioration in concrete bridges.” Transp. Res. Rec. 2201 (1): 27–33. https://doi.org/10.3141/2201-04.
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Published In
Journal of Performance of Constructed Facilities
Volume 34 • Issue 5 • October 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Aug 30, 2019
Accepted: Mar 31, 2020
Published online: Jun 25, 2020
Published in print: Oct 1, 2020
Discussion open until: Nov 25, 2020
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