An application of Markov chains to predict the evolution of performance indicators based on pavement historical data

References

• AASHTO , 2001. AASHTO Pavement Management Guide . Washington, DC: AASHTO.
   Google Scholar
• Abaza, K.A. , 2006. Iterative linear approach for nonlinear nonhomogenous stochastic pavement management models. Journal of Transportation Engineering , 132 (3), 244–256.
   Web of Science ®Google Scholar
• Abaza, K.A. , 2014. Back-calculation of transition probabilities for Markovian-based pavement performance prediction models. International Journal of Pavement Engineering , 17 (3), 1–12.
   Web of Science ®Google Scholar
• Abo-Hashema, M.A. and Sharaf, E.A. , 2009. Development of maintenance decision model for flexible pavements. International Journal of Pavement Engineering , 10 (3), 173–187.
   Web of Science ®Google Scholar
• Baik, H. , Jeong, H. and Abraham, D. , 2006. Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems. Journal of water resources planning and management , 132 (1), 15–24.
   Web of Science ®Google Scholar
• Bayomy, F. , Salem, H. , and Vosti, L. , 2006. Analysis of the Long-term Pavement Performance Data for the Idaho GPS and SPS Sections, Moscow: Idaho Transportation Department, 123 p.
   Google Scholar
• Bi, J. and Bennett, K. , 2003. Regression error characteristic curves. In: Proceedings of the Twentieth International Conference on Machine Learning, August. Washington, DC: AAAI Press, 43–50.
   Google Scholar
• Bladt, M. and Sørensen, M. , 2009. Efficient estimation of transition rates between credit ratings from observations at discrete time points. Quantitative Finance , 9 (2), 147–160.
   Web of Science ®Google Scholar
• Butt, A. , et al. , 1987. Pavement performance prediction model using the Markov process. Transportation Research Board . 1123, 12–19.
   Google Scholar
• Ching, W.K. and Ng, M.K. , 2006. Markov Chains: Models, Algorithms and Applications . 1st ed. New York: Springer.
   Google Scholar
• Elbehairy, H. , 2007. Bridge management system with integrated life cycle cost optimization. Thesis (PhD). University of Waterloo, Ontario.
   Google Scholar
• Hall, K.T. , Simpson, A.L. , and Correa, C.E. , 2002. LTPP data analysis: effectiveness of maintenance and rehabilitation options. National Cooperative Highway Research Program , Washington, DC, 332 p.
   Google Scholar
• Hassan, R. , Lin, O. , and Thananjeyan, A. , 2015. Probabilistic modelling of flexible pavement distresses for network management. International Journal of Pavement Engineering . doi:10.1080/10298436.2015.1065989.
   Web of Science ®Google Scholar
• Hastie, T. , Tibshirani, R. , and Friedman, J. , 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction . 2nd ed. New York: Springer-Verlag.
   Google Scholar
• Hong, H.P. and Wang, S.S. , 2003. Stochastic modeling of pavement performance. International Journal of Pavement Engineering , 4 (4), 235–243.
   Google Scholar
• Jackson, C.H. , 2011. Multi-state models for panel data: the msm package for R. Journal of Statistical Software , 38 (8), 1–29.
   Web of Science ®Google Scholar
• JAE , 1995. Manual of Pavement Structures for the Portuguese Road Network . Lisbon: Junta Autónoma de Estradas (in portuguese).
   Google Scholar
• Kalbfleisch, J.D. and Lawless, J.F. , 1985. The analysis of panel data under a Markov assumption. Journal of the American Statistical Association , 80 (392), 863–871.
   Web of Science ®Google Scholar
• Kallen, M. and Van Noortwijk, J. , 2006. Statistical inference for Markov deterioration models of bridge conditions in the Netherlands. In Proceedings of the third international conference on bridge maintenance, safety and management (IABMAS) , Porto, Portugal, 16–19.
   Google Scholar
• Khattak, M.J. and Alrashidi, M. , 2013. Performance of preventive maintenance treatments of flexible pavements. International Journal of Pavement Research and Technology , 6 (3), 184–196.
   Google Scholar
• Kobayashi, K. , Do, M. and Han, D. , 2010. Estimation of Markovian transition probabilities for pavement deterioration forecasting. KSCE Journal of Civil Engineering , 14 (3), 343–351.
   Web of Science ®Google Scholar
• Kobayashi, K. , Kaito, K. and Lethanh, N. , 2012. A statistical deterioration forecasting method using hidden Markov model for infrastructure management. Transportation Research Part B: Methodological , 46 (4), 544–561.
   Web of Science ®Google Scholar
• Lee, J. and Madanat, S. , 2014. Joint optimization of pavement design, resurfacing and maintenance strategies with history-dependent deterioration models. Transportation Research Part B: Methodological , 68, 141–153.
   Web of Science ®Google Scholar
• Lethanh, N. , Kaito, K. and Kobayashi, K. , 2014. Infrastructure deterioration prediction with a Poisson hidden Markov model on time series data. Journal of Infrastructure Systems , 21 (3), 04014051. Available from: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29IS.1943-555X.0000242.
   Web of Science ®Google Scholar
• Li, N. , Xie, W.C. and Haas, R. , 1996. Reliability-based processing of Markov chains for modeling pavement network deterioration. Transportation Research Record: Journal of the Transportation Research Board , 1524, 203–213.
   Google Scholar
• Litzka, J. , et al. , 2008. The way forward for pavement performance indicators across Europe - COST Action 354: Performance Indicators for Road Pavements . Vienna: FSV -- Austrian Transportation Research Association.
   Google Scholar
• Lounis, Z. and Madanat, S.M. , 2002. Integrating mechanistic and statistical deterioration models for effective bridge management. In: 7th ASCE international conference on applications of advanced technology in transportation . Boston, 513–520.
   Google Scholar
• Medury, A. and Madanat, S. , 2014. Simultaneous network optimization approach for pavement management systems. Journal of Infrastructure Systems , 20 (3), 04014010.
   Web of Science ®Google Scholar
• Meneses, S. and Ferreira, A. , 2014. Flexible pavement maintenance programming considering the minimisation of maintenance and rehabilitation costs and the maximisation of the residual value of pavements. International Journal of Pavement Engineering , 16 (7), 571–586.
   Web of Science ®Google Scholar
• Mishalani, R.G. and Madanat, S.M. , 2002. Computation of infrastructure transition probabilities using stochastic duration models. Journal of Infrastructure Systems , 8 (4), 139–148.
   Google Scholar
• Morcous, G. , 2006. Performance prediction of bridge deck systems using Markov chains. Journal of Performance of Constructed Facilities , 20 (2), 146–155.
   Web of Science ®Google Scholar
• Ng, S.K. and Moses, F. , 1998. Bridge deterioration modeling using semi-Markov theory. A. A. Balkema Uitgevers B. V, Structural Safety and Reliability. , 1, 113–120.
   Google Scholar
• Nii, L. and Mills, O. , 2010. Hierarchical Markov chain Monte Carlo and pavement roughness model. Thesis (PhD). University of Delaware.
   Google Scholar
• Odoki, J.B. and Kerali, H.R. , 2013. HDM-4 Volume 4: Analytical Framework and Model Descriptions . Paris: World Road Association (PIARC).
   Google Scholar
• Panthi, K. , 2009. A methodological framework for modeling pavement maintenance costs for projects with performance-based contracts. Thesis (PhD). Florida International University.
   Google Scholar
• R Development Core Team , 2009. R: A Language and Environment for Statistical Computing . Vienna, Autria: R Foundation for Statistical Computing. Available from: http://www.r-project.org/.
   Google Scholar
• Thompson, P. and Johnson, M. , 2005. Markovian bridge deterioration: developing models from historical data. Structure and Infrastructure Engineering , 1 (1), 85–91.
   Web of Science ®Google Scholar
• Wu, Z. , et al. , 2012. Framework for multiobjective optimization of physical highway assets investments. Journal of Transportation Engineering , 138 (12), 1411–1421.
   Web of Science ®Google Scholar
• Yang, J. , et al. , 2005. Use of recurrent Markov chains for modeling the crack performance of flexible pavements. Journal of Transportation Engineering , 131 (11), 861–872.
   Web of Science ®Google Scholar
• Yang, Y. , Pam, H. and Kumaraswamy, M. , 2009. Framework development of performance prediction models for concrete bridges. Journal of Transportation Engineering , 135 (8), 545–554.
   Web of Science ®Google Scholar