Artificial Neural Network Model of Bridge Deterioration
Publication: Journal of Performance of Constructed Facilities
Volume 24, Issue 6
Abstract
Accurate prediction of bridge condition is essential for the planning of maintenance, repair, and rehabilitation. An examination of the assumptions (for example, maintenance independency) of the existing Markovian model reveals possible limitations in its ability to adequately model the procession of deterioration for these purposes. This study uses statistical analysis to identify significant factors influencing the deterioration and develops an application model for estimating the future condition of bridges. Based on data derived from historical maintenance and inspection of concrete decks in Wisconsin, this study identifies 11 significant factors and develops an artificial neural network (ANN) model to predict associated deterioration. An analysis of the application of ANN finds that it performs well when modeling deck deterioration in terms of pattern classification. The developed model has the capacity to accurately predict the condition of bridge decks and therefore provide pertinent information for maintenance planning and decision making at both the project level and the network level.
Get full access to this article
View all available purchase options and get full access to this article.
References
Aleksander, I., and Morton, H. (1990). An introduction to neural computing, Chapman and Hall, London.
Arditi, D., and Tokdemir, O. B. (1999). “Predicting the outcome of construction litigation using case based reasoning.” Comput. Aided Civ. Infrastruct. Eng., 14(6), 385–393.
Arima, H., Tanaka, S., and Matsui, S. (1999). “Maintenance system of paint on steel bridges.” Technology Reports of the Osaka University, 49(2358), 171–178.
Attoh-Okine, N. O. (2001). “Grouping pavement condition variables for performance modeling using self-organizing maps.” Comput. Aided Civ. Infrastruct. Eng., 16(2), 112–125.
Babaei, K., Purvis, R. L., Clear, K. C., and Weyer, R. E. (1996). Methodology for concrete removal, protection, and rehabilitation, DOT, Federal Highway Administration, Washington, D.C.
Bettigole, N., and Robison, R. (1997). Bridge decks: Design, construction, rehabilitation, replacement, ASCE, Reston, Va.
Busa, G. D., Ben-Akiva, M. E., and Buyukozturk, O. (1985). Modeling concrete deck deterioration, DOT, Transportation Systems Center, Cambridge, Mass.
Cady, P. D., and Weyers, R. E. (1992). “Predicting service life of concrete bridge decks subject to reinforcement corrosion.” Corrosion Forms and Control for Infrastructure, ASTM STP, 1137, 328–338.
Frangopol, D. M., Kong, J. S., and Gharaibeh, E. S. (2001). “Reliability-based life-cycle management of highway bridges.” J. Comput. Civ. Eng., 15(1), 27–34.
Hung, S. L., Kao, C. Y., and Lee, J. C. (2000). “Active pulse structural control using artificial neural networks.” J. Eng. Mech., 126(8), 839–849.
Hyman, W., Hughes, D., and Dobson, T. (1983). “The least cost mix of bridge replacement and repair work on Wisconsin’s state highways over time—A computer simulation.” Technical Rep., WisDOT, Madison, Wis.
Jha, M. K., and Abdullah, J. (2006). “A Markovian approach for optimizing highway life-cycle with genetic algorithms by considering maintenance of roadside appurtenances.” J. Franklin Inst., 343(4–5), 404–419.
Kim, S. -H., Yoon, C., and Kim, B. -J. (2000). “Structural monitoring system based on sensitivity analysis and a neural network.” Comput. Aided Civ. Infrastruct. Eng., 15(4), 189–195.
Kirkpatrick, T. J., Weyers, R. E., Anderson-Cook, C. M., and Sprinkel, M. M. (2002). “Probabilistic model for the chloride-induced corrosion service life of bridge decks.” Cem. Concr. Res., 32(12), 1943–1960.
Kleywegt, A. J., and Sinha, K. C. (1994). “Tools for bridge management data analysis.” Transp. Res. Circ., 423, 16–26.
Madanat, S., Mishalani, R., and Wan Ibrahim, W. H. (1995). “Estimation of infrastructure transition probabilities from condition rating data.” J. Infrastruct. Syst., 1(2), 120–125.
Madanat, S. M., Karlaftis, M. G., and McCarthy, P. S. (1997). “Probabilistic infrastructure deterioration models with panel data.” J. Infrastruct. Syst., 3(1), 4–9.
Mauch, M., and Madanat, S. (2001). “Semiparametric hazard models of reinforced concrete bridge deck deterioration.” J. Infrastruct. Syst., 7(2), 49–57.
Morcous, G. (2006). “Performance prediction of bridge deck systems using Markov chains.” J. Perform. Constr. Facil., 20(2), 146–155.
Mukherjee, A., Deshpande, J. M., and Anmala, J. (1996). “Prediction of buckling load of columns using artificial neural networks.” J. Struct. Eng., 122(11), 1385–1387.
Rattanasuwan, P. (1998). “Estimating NBI ratings using a neural network.” MS thesis, Univ. of Wisconsin-Madison, Madison, Wis.
Robelin, C. A., and Madanat, S. M. (2007). “History-dependent bridge deck maintenance and replacement optimization with Markov decision processes.” J. Infrastruct. Syst., 13(3), 195–201.
Rumelhart, D. E., Hinton, G. E., and McClelland, J. L. (1986a). “A general framework for parallel distributed processing.” Parallel Distributed Processing, 1, 45–76.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986b). “Learning internal representations by error propagation.” Parallel Distributed Processing, 1, 318–362.
Saito, M., and Fan, J. (2000). “Artificial neural network-based heuristic optimal traffic signal timing.” Comput. Aided Civ. Infrastruct. Eng., 15(4), 293–307.
Scott, M., et al. (2003). “A comparison of nondestructive evaluation methods for bridge deck assessment.” NDT & E Int., 36(4), 245–255.
Sobanjo, J. O. (1997). “A neural network approach to modeling bridge deterioration.” Computing in Civil Engineering, Proc., 4th Congress, ASCE, Philadelphia, 623–626.
Tao, Z., Corotis, R. B., and Ellis, J. H. (1994). “Reliability-based bridge design and life cycle management with Markov decision processes.” Struct. Safety, 16(1–2), 111–132.
Thompson, P. D., Small, E. P., Johnson, M., and Marshall, A. R. (1998). “The PONTIS bridge management system.” Struct. Eng. Int. (IABSE, Zurich, Switzerland), 8(4), 303–308.
West, H. H., McClure, R. M., Gannon, E. J., Raid, H. L., and Siverling, B. B. (1989). A nonlinear deterioration model for the estimation of bridge design life, The Pennsylvania Transportation Institute, University Park, Pa.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 24 • Issue 6 • December 2010
Pages: 597 - 602
Copyright
© 2010 ASCE.
History
Received: Aug 26, 2009
Accepted: Jan 30, 2010
Published online: Nov 15, 2010
Published in print: Dec 2010
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.