Comparative Probabilistic Initial Bridge Load Rating Model
Publication: Journal of Bridge Engineering
Volume 12, Issue 6
Abstract
A model that applies to initial bridge load ratings based on allowable stress, load factor, and load and resistance factor design is presented. The prognostic component calculates bridge load ratings probabilistically considering random variables representative of load and resistance effects. The diagnostic component helps to point out the sensitivity rankings of these input parameters. Monte Carlo simulation and Bayesian networks are the tools employed in this bidirectional model. The model is applied to a beam in a prestressed concrete bridge at the beginning of its service life to demonstrate conclusions related to its applicability as a tool in areas related to bridge design and load rating.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
Support from NSF Career Development Award No. NSFCMS-9702656 is appreciated. The work presented here is designed to adapt to the use of quantifiable nondestructive test measurements. Thus, modest support from the NSF funded Center for Subsurface Sensing and Imaging Systems (CenSSIS; Award No. NSFEEC-9986821) is appreciated.
References
Akgül, F., and Frangopol, D. M. (2004). “Bridge rating and reliability correlation: comprehensive study for different bridge types.” J. Struct. Eng., 130(7), 1063–1074.
AASHTO. (1994). Manual for condition evaluation of bridges, Washington, D.C.
AASHTO. (1996). Standard specification for highway bridges, 16th Ed., Washington, D.C.
AASHTO. (1998). AASHTO LRFD bridge design specifications, 2nd Ed., Washington, D.C.
AASHTO. (2003). Manual for condition evaluation and load and resistance factor rating (LRFR) of highway bridges, Washington, D.C.
Bhattacharya, B., Li, D., Chajes, M., and Hastings, J. (2005). “Reliability-based load and resistance factor rating using in-service data.” J. Bridge Eng., 10(5), 530–543.
Borsuk, M. E., Stow, C. A., and Reckhow, K. H. (2003). “Integrated approach to total maximum daily load development for Neuse River Estuary using Bayesian probability network model (Neu-BERN).” J. Water Resour. Plann. Manage., 129(4), 271–282.
Charniak, E. (1991). “Bayesian networks without tears.” AI Mag., 12(4), 50–63.
El-Tawil, S., and Okeil, A. M. (2002). “LRFD flexural provisions for prestressed concrete bridge girders strengthened with carbon fiber-reinforced polymer laminates.” ACI Struct. J., 99(2), 181–190.
Federal Highway Administration (FHWA). (2006). “LRFD implementation plan initial draft-load and resistance factor design—Bridge.” http://fhwa.dot.gov/bridge/lrfd/plan.cfm (April 27, 2006).
Gilbertson, C., and Ahlborn, T. (2004). “A probabilistic comparison of prestress loss methods in prestressed concrete beams.” PCI J., 49(5), 52–69.
Hamann, R., and Bulleit, W. (1987). “Reliability of prestressed high strength concrete beams in flexure.” Proc., 5th Int. Conf. on Applications of Statistics and Probability in Soil and Structural Engineering, Vancouver, BC, Canada, 141–147.
Hahn, M. A., Palmer, R. N., Merrill, M. S., and Lukas, A. B. (2002). “Expert system for prioritizing the inspection of sewers: Knowledge base formulation and evaluation.” J. Water Resour. Plann. Manage., 128(2), 121–129.
Heckerman, D., Mamdani, E. H., and Wellman, M. P. (1995). “Real world applications of Bayesian networks.” Commun. ACM, 38(3), 24–26.
Kadie, C. M., Hovel, D., and Horvitz, E. (2001). “MSBNx: A component-centric toolkit for modeling and inference with Bayesian networks.” Microsoft Research Technical Rep. No. MSR-TR-2001-67, Redmond, Wash.
Lichtenstein Consulting Engineers, Inc. (2001). “NCHRP Web document 28: Contractor’s final report: Manual for condition evaluation and load rating of highway bridges using load and resistance factor philosophy.” Transportation Res. Board Project No. C12-46, http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w28.pdf (October 13, 2006).
Lwin, M. M. (2006). “Bridge load ratings for the National Bridge Inventory-National Bridge Inspection Standards.” FHWA Policy Memorandum, http://www.fhwa.dot.gov/bridge/nbis/103006.cfm (March 12, 2007).
MacGregor, J. G., Mirza, S. A., and Ellingwood, B. (1983). “Statistical analysis of resistance of reinforced and prestressed concrete members.” ACI J., 80(3), 167–176.
McCabe, B., AbouRizk, S. M., and Goebel, R. (1998). “Belief networks for construction performance diagnostics.” J. Comput. Civ. Eng., 12(2), 93–100.
Mirza, S. A., MacGregor, J. G., and Hatzinikolas, M. (1979). “Statistical descriptions of strength of concrete.” J. Struct. Div., 105(6), 1021–1037.
Naaman, A. E., and Siriaksorn, A. (1982). “Reliability of partially prestressed concrete beams at serviceability limit states.” PCI J., 27(6), 66–85.
Nowak, A. S. (1993). “Calibration of LRFD bridge design code.” NCHRP Project No. 12-33. Univ. of Michigan, Ann Arbor, Mich.
Precast/Prestressed Concrete Institute (PCI). (2003). Precast prestressed concrete bridge design manual, Chicago.
Rogers, B. J., and Jauregui, D. V. (2005). “Load rating of prestressed concrete girder bridges: Comparative analysis of load factor rating and load and resistance factor rating.” Transportation Research Record. 1928, Transportation Research Board, Washington, D.C., 53–63.
Sahely, B. S. G. E., and Bagley, D. M. (2001). “Diagnosing upsets in anaerobic wastewater treatment using Bayesian belief networks.” J. Environ. Eng., 127(4), 302–310.
Sirca, Jr., G. F., and Adeli, H. (2005). “Case-based reasoning for converting working stress design-based bridge ratings to load factor design-based ratings.” J. Bridge Eng., 10(4), 450–459.
Steinberg, E. P. (1995). “Probabilistic assessment of prestress loss in pretensioned prestressed concrete.” PCI J., 40(6), 76–85.
Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 12 • Issue 6 • November 2007
Pages: 785 - 793
Copyright
© 2007 ASCE.
History
Received: Mar 1, 2006
Accepted: Mar 16, 2007
Published online: Nov 1, 2007
Published in print: Nov 2007
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.