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Technical Papers
Jul 17, 2017

Determination of Markov Transition Probabilities to be Used in Bridge Management from Mechanistic-Empirical Models

Publication: Journal of Bridge Engineering
Volume 22, Issue 10

Abstract

Many bridge management systems use Markov models to predict the future deterioration of structural elements. This information is subsequently used in the determination of optimal intervention strategies and intervention programs. The input for these Markov models often consists of the condition states of the elements and how they have changed over time. This input is used to estimate the probabilities of transition of an object from each possible condition state to each other possible condition state in one time period. A complication in using Markov models is that there are situations in which there is an inadequate amount of data to estimate the transition probabilities using traditional methods (e.g., due to the lack of recording past information so that it can be easily retrieved, or because it has been collected in an inconsistent or biased manner). In this paper, a methodology to estimate the transition probabilities is presented that uses proportional data obtained by mechanistic-empirical models of the deterioration process. A restricted least-squares optimization model is used to estimate the transition probabilities. The methodology is demonstrated by using it to estimate the transition probabilities for a reinforced concrete (RC) bridge element exposed to chloride-induced corrosion. The proportional data are generated by modeling the corrosion process using mechanistic-empirical models and Monte Carlo simulations.

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References

Al-Subhi, K. M., Johnston, D. W., and Farid, F. (1989). “Optimizing system level bridge maintenance rehabilitation and replacement decisions.” FHWA/NC/89-001, 3North Carolina State Univ., Raleigh, NC.
Diamond, S., and Boyd, S. (2016). “CVXPY: A Python-embedded modeling language for convex optimization.” J. Mach. Learn. Res., 17(83), 1–7.
DuraCrete (1999). “Models for environmental actions on concrete structures.” Research Project No. BE95-1347/R3, 3The European Union: Brite EuRam III, Brussels, Belgium.
DuraCrete (2000a). “General guidelines for durability design and redesign.” Research Project No. BE95-1347/R17, 3The European Union: Brite EuRam III, Brussels, Belgium.
DuraCrete (2000b). “Statistical quantification of the variables in the limit state functions.” Research Project No. BE95-1347/R9, 3The European Union: Brite EuRam III, Brussels, Belgium.
Fernando, D., Adey, B. T., and Lethanh, N. (2015). “A model for the evaluation of intervention strategies for bridges affected by manifest and latent deterioration processes.” Struct. Infrastruct. Eng., 11(11), 1466–1483.
FHWA (Federal Highway Administration). (2002). “Managing bridges the Pontis way.” FHWA-RD-02-012 of the focus on accelerating infrastructure innovation (4.5 ed.), U.S. DOT, Washington, DC.
fib (International Federation for Structural Concrete). (2006). “Model code for service life design.” Tech. rep. fib bulletin 34, Lausanne, Switzerland.
Golabi, K., and Shepard, R. (1997). “Pontis: A system for maintenance optimization and improvement of us bridge networks.” Interfaces, 27(1), 71–88.
Goodman, L. A. (1953). “A further note on finite Markov processes in psychology.” Psychometrika, 18(3), 245–248.
Hajdin, R. (2003). “Bridge management strategies and structural reliability.” Proc., Life-Cycle Cost Analysis and Design and Management, 3rd Int. Workshop on Life-Cycle Cost Analysis and Design of Civil Infrastructure Systems, D. Frangopol, E. Brühwiler, M. Faber, and B. Adey, eds., ASCE, Reston, VA, 319–327.
Hajdin, R. (2006). “KUBA version 4.0.” Proc., IABSE Conf.: Operation, Maintenance and Rehabilitation of Large Infrastructure Projects, Bridges and Tunnels, International Association for Bridge and Structural Engineering, Zurich, Switzerland, 9–16.
Jiang, Y., Saito, M., and Sinha, K. (1988). “Bridge performance prediction model using the Markov chain.” Transportation Research Record, 1180, 25–32.
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.
Kobayashi, K., Kaito, K., and Lethanh, N. (2012). “A Bayesian estimation method to improve deterioration prediction for infrastructure system with Markov chain model.” Int. J. Archit. Eng. Constr., 1(1), 1–13.
Kuba-MS 5.1 [Computer software]. Federal Dept. of Highways, Bern, Switzerland.
Lancaster, T. (1990). The econometric analysis of transition data, Cambridge University Press, Cambridge, U.K.
Lee, T. C., Judge, G. G., and Zellner, A. (1970). Estimating the parameters of the Markov probability model from aggregate time series data, North-Holland, Amsterdam, Netherlands.
Lee, T. C., Judge, G. G., and Zellner, A. (1972). “Estimating the parameters of the Markov probability model from aggregate time series data.” J. Econ. Lit., 10(1), 88–90.
Lethanh, N., Adey, B. T., and Fernando, D. N. (2015a). “Optimal intervention strategies for multiple objects affected by manifest and latent deterioration processes.” Struct. Infrastruct. Eng., 11(3), 389–401.
Lethanh, N., Kaito, K., and Kobayashi, K. (2015b). “Infrastructure deterioration prediction with a Poisson hidden Markov model on time series data.” J. Infrastruct. Syst., 04014051.
Life-365 Consortium III. (2013). “Life-365 2.2 service life prediction model: And computer program for predicting the service life and life-cycle cost of reinforced concrete exposed to chlorides user’s manual.” Life-365 Consortium III, Washington, DC.
Miller, G. A. (1952). “Finite Markov processes in psychology.” Psychometrika, 17(2), 149–167.
Mishalani, R., and Madanat, S. (2002). “Computation of infrastructure transition probabilities using stochastic duration models.” J. Infrastruct. Syst., 139–148.
Pontis 4.1 [Computer software]. Cambridge Systematics, Inc., Washington, DC.
Python CVXPY 1.0 [Computer software]. Python Software Foundation, Wilmington, DE.
rlstpe 1.0.0 [Computer software]. J. Hackl, Zurich, Switzerland.
Robelin, C., and Madanat, S. (2007). “History-dependent bridge deck maintenance and replacement optimization with Markov decision processes.” J. Infrastruct. Syst., 195–201.
Roelfstra, G., Adey, B., Hajdin, R., and Brühwiler, E. (1999). “The condition evaluation of concrete bridges based on a segmental approach, nondestructive testing methods, and deterioration models.” Proc., 8th Int. Bridge Management Conf., Committee on Bridge Maintenance and Management, National Research Council, Transportation Research Board, Washington, DC.
Roelfstra, G., Hajdin, R., Adey, B., and Brühwiler, E. (2004). “Condition evolution in bridge management systems and corrosion-induced deterioration.” J. Bridge Eng., 268–277.
Soderqvist, M.-K., and Veijola, M. (1998). “The Finnish bridge management system.” Struct. Eng. Int., 8(4), 315–319.
Thompson, P., Merlo, T., Kerr, B., Cheetham, A., and Ellis, R. (1999). “The new Ontario bridge management system.” Proc., 8th Int. Bridge Management Conf., Denver.
Thompson, P. D., Small, E. P., Johnson, M., and Marshall, A. R. (1998). “The PONTIS bridge management system.” Struct. Eng. Int., 8(4), 303–308.
Tsuda, Y., Kaito, K., Aoki, K., and Kobayashi, K. (2006). “Estimating Markovian transition probabilities for bridge deterioration forecasting.” J. Struct. Eng. Earthquake Eng., 23(2), 241–256.
Tuutti, K. (1982). Corrosion of steel in concrete, Swedish Cement and Concrete Research Institute, Stockholm, Sweden.
VTT Technical Research Centre of Finland. (2003). “LIFECON: Life cycle management of concrete infrastructures for improved sustainability.” Tech. Rep. No. D 3.2, Espoo, Finland.
Walbridge, S., Fernando, D., and Adey, B. (2013). “Total cost-benefit analysis of alternative corrosion management strategies for a steel roadway bridge.” J. Bridge. Eng., 318–327.

Information & Authors

Information

Published In

Go to Journal of Bridge Engineering
Journal of Bridge Engineering
Volume 22Issue 10October 2017

History

Received: Apr 5, 2016
Accepted: Apr 21, 2017
Published online: Jul 17, 2017
Published in print: Oct 1, 2017
Discussion open until: Dec 17, 2017

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Authors

Affiliations

Ph.D. Principal, POM+ Consulting, Ltd., 2001B-C1-Ecopark, HungYen 160000, Vietnam. ORCID: https://orcid.org/0000-0002-0163-4529. E-mail: [email protected]
Jürgen Hackl [email protected]
Ph.D. Candidate, Institute of Construction and Infrastructure Management, Swiss Federal Institute of Technology (ETH), 8093 Zürich, Switzerland (corresponding author). E-mail: [email protected]
Bryan T. Adey, Ph.D. [email protected]
Professor, Institute of Construction and Infrastructure Management, Swiss Federal Institute of Technology (ETH), 8093 Zürich, Switzerland. E-mail: [email protected]

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