Using Classification Trees for Predicting National Bridge Inventory Condition Ratings
Publication: Journal of Infrastructure Systems
Volume 19, Issue 4
Abstract
In bridge management practice, bridge condition is the fundamental information needed to allow decision makers to make well-informed decisions regarding preservation, rehabilitation, or replacement of a bridge or network of bridges. The National Bridge Inventory (NBI) condition ratings, collected since the early 1970s, and the Commonly Recognized (CoRe) element condition data, collected since the early 1990s, are two major sources of bridge condition data in the United States. General NBI condition ratings are utilized for performance assessment, performance reporting, resource allocation, and selection of bridge projects by all levels of government. Since the early 1990s, the bridge management community has been interested in an algorithm to predict the categorical NBI condition rating classes from the more quantitative and detailed CoRe element condition data. An algorithm with sufficient predictive accuracy would make only CoRe element inspections necessary and would provide time and resource savings. This paper presents a new methodology for this purpose, using classification and regression trees (CARTs). The CART analyses were conducted with the bridge condition data provided by three state transportation agencies, using data from 2006 to 2010. The statistical results point to a more accurate prediction method than the previous algorithms described in the literature.
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References
Aldemir-Bektas, B., and Smadi, O. G. (2008). “A discussion on the efficiency of NBI Translator algorithm.” Proc., 10th Int. Conf. on Bridge and Structure Management, Transportation Research E-Circular E-C128, Transportation Research Board, Washington, DC.
Al-Wazeer, A., Nutakor, C., and Harris, B. (2007). “Comparison of neural network method versus National Bridge Inventory Translator in predicting bridge condition ratings.” TRB 86th Annual Meeting Compendium of Papers (CD-ROM), Transportation Research Board, Washington, DC.
Bektas, B. A. (2011). “Bridge management from data to policy.” Ph.D. thesis, Iowa State Univ., Ames, IA.
Breiman, L., Freidman, J., Stone, C., and Olshen, R. (1984). Classification and regression trees, Chapman & Hall, New York.
De’ath, G., and Fabricius, K. E. (2000). “Classification and regression trees: A powerful yet simple technique for ecological data analysis.” Ecology, 81(11), 3178–3192.
Federal Highway Administration (FHWA). (1995). “Recording and coding guide for the structure inventory and appraisal of the nation’s bridges.” Washington, DC.
Federal Highway Administration (FHWA). (2009). “2008 status of the nation’s highways, bridges, and transit: Condition and performance, report to the Congress.” Washington, DC.
Gelfand, S. B., Ravishankar, C. S., and Delp, E. J. (1989). “An iterative growing and pruning algorithm for classification tree design.” Systems, Man and Cybernetics, 1989 Conf. Proc., IEEE Int. Conf., IEEE, New York.
Hale, J. E., Hale, D. P., and Sharpe, S. (2007). “Asset management GASB 34 compliance phase III (bridges), final report.” Aging Infrastructure Systems Center of Excellence and University Transportation Center for Alabama, AL.
Hearn, G., Cavallin, J., and Frangopol, D. M. (1997). “Generation of NBI condition ratings from condition reports for Commonly Recognized (CoRe) elements.” Univ. of Colorado at Boulder, Boulder, CO.
Hearn, G., Frangopol, D., Chakravorty, M., Myers, S., Pinkerton, B., and Siccardi, A. J. (1993). “Automated generation of NBI reporting fields from Pontis BMS database.” Infrastructure: Planning and Management, J. L. Gifford, D. R. Uzarski, and S. McNeil, eds., ASCE, Reston, VA, 226–230.
Herabat, P., and Tangphaisankun, A. (2005). “Multi-objective optimization model using constraint-based genetic algorithms for Thailand pavement management.” J. Eastern Asia Soc. Transp. Studies, 6, 1137–1152.
Lichtenstein, A. G. (1993). “The Silver Bridge collapse recounted.” J. Perform. Constr. Facilities, 7(4), 249–261.
Loh, W.-Y. (2011). “Classification and regression trees.” Data Min. Knowl. Discov., 1(1), 14–23.
Nagelkerke, N. J. D. (1991). “A note on a general definition of the coefficient of determination.” Biometrika, 78(3), 691–692.
Pontis Bridge Management Release 4.4 [Computer software]. Cambridge Systematics, Cambridge, MA.
SAS JMP Version 9 [Computer software]. SAS Publishing, Cary, NC.
Shepard, R. W., and Johnson, M. B. (1999). “California bridge health index.” Proc., 8th Int. Bridge Management Conf, Transportation Research Board, Denver, CO.
Sobanjo, J. O., Thompson, P. D., and Kerr, R. (2008). “Element-to-component translation of bridge condition ratings.” Transportation Research Board Annual Meeting 2008 Compendium of Papers DVD, Transportation Research Board, Washington, DC.
Vayssières, M. P., Plant, R. E., and Allen-Diaz, B. H. (2000). “Classification trees: An alternative non-parametric approach for predicting species distributions.” J. Veg. Sci., 11(5), 679–694.
Information & Authors
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Published In
Journal of Infrastructure Systems
Volume 19 • Issue 4 • December 2013
Pages: 425 - 433
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Apr 30, 2012
Accepted: Dec 12, 2012
Published online: Dec 14, 2012
Discussion open until: May 14, 2013
Published in print: Dec 1, 2013
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