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Civil & Environmental Engineering

Theoretical framework for modeling the long-term performance of pavement routine maintenance using Markov chain

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Article: 2366529 | Received 30 Jan 2024, Accepted 03 Jun 2024, Published online: 01 Jul 2024

References

  • Abaza, K. A. (2017). Empirical Markovian-based models for rehabilitated pavement performance used in a life cycle analysis approach. Structure and Infrastructure Engineering, 13(5), 625–636. https://doi.org/10.1080/15732479.2016.1187180
  • Abaza, K. A. (2021). Optimal novel approach for estimating the pavement transition probabilities used in Markovian prediction models. International Journal of Pavement Engineering, 23(8), 2809–2820. https://doi.org/10.1080/10298436.2021.1873326
  • Abaza, K. A. (2022). Simplified exhaustive search approach for estimating the non-homogeneous transition probabilities for infrastructure asset management. Journal of Infrastructure Systems, 28(1), 04021048. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000660
  • Abaza, K. A., & Murad, M. (2009). Predicting flexible pavement remaining strength and overlay design thickness with stochastic modeling. Transportation Research Record: Journal of the Transportation Research Board, 2094(1), 62–70. https://doi.org/10.3141/2094-07
  • Abaza, K. A., & Murad, M. M. (2007). Dynamic probabilistic approach for a long-term pavement restoration program with added user cost. Transportation Research Record: Journal of the Transportation Research Board, 1990(1), 48–56. https://doi.org/10.3141/1990-06
  • Abed, A., Thom, N., & Neves, L. (2019). Probabilistic prediction of asphalt pavement performance. Road Materials and Pavement Design, 20(Suppl 1), S247–S264. https://doi.org/10.1080/14680629.2019.1593229
  • Alonso-Solorzano, Á., Pérez-Acebo, H., Findley, D. J., & Gonzalo-Orden, H. (2023). Transition probability matrices for pavement deterioration modeling with variable duty cycle time. International Journal of Pavement Engineering, 24(2), 1–18. https://doi.org/10.1080/10298436.2023.2278694
  • Amin, M. S. R. (2015). The pavement performance modeling: deterministic vs. stochastic approaches. In Numerical methods for reliability and safety assessment (pp. 179–196). Springer.
  • Ansarilari, Z., & Golroo, A. (2020). Integrated airport pavement management using a hybrid approach of Markov Chain and supervised multi-objective genetic algorithms. International Journal of Pavement Engineering, 21(14), 1864–1873. https://doi.org/10.1080/10298436.2019.1571208
  • ASTM. (2007). Standard practice for roads and parking lots, pavement condition index surveys. ASTM.
  • Butt, A., Shahin, M., Feighan, K., & Carpenter, S. (1987). Pavement performance prediction model using the Markov process. In Transportation research record 1123 (12–19). TRB.
  • Costello, S. B., Ortiz-Garcia, J. J., & Snaith, M. S. (2016). Analytical tool for calculating transition probabilities for pavement performance prediction. Road Transport Res, 25(2), 30–39.
  • Dalla Rosa, F., Liu, L., & Gharaibeh, N. G. (2017). IRI prediction model for use in network-level pavement management systems. Journal of Transportation Engineering, Part B: Pavements, 143(1), 04017001. https://doi.org/10.1061/JPEODX.0000003
  • Fuentes, L., Camargo, R., Arellana, J., Velosa, C., & Martinez, G. (2021). Modelling pavement serviceability of urban roads using deterministic and probabilistic approaches. International Journal of Pavement Engineering, 22(1), 77–86. https://doi.org/10.1080/10298436.2019.1577422
  • Fwa, T. F., & Sinha, K. C. (1986). A study of the effects of routine pavement maintenance. Transportation Research Record, 1102, 6–13.
  • Galvis Arce, O. D., & Zhang, Z. (2021). Skid resistance deterioration model at the network level using Markov chains. International Journal of Pavement Engineering, 22(1), 118–126. https://doi.org/10.1080/10298436.2019.1578882
  • Hafez, M., Ksaibati, K., & Atadero, R. (2021). Pavement maintenance practices of low-volume roads and potential enhancement: The regional experience of Colorado pavement management system. International Journal of Pavement Engineering, 22(6), 718–731. https://doi.org/10.1080/10298436.2019.1643021
  • Hajj, E. Y., Loria, L., & Sebaaly, P. E. (2010). Performance evaluation of asphalt pavement preservation activities. Transportation Research Record: Journal of the Transportation Research Board, 2150(1), 36–46. https://doi.org/10.3141/2150-05
  • Hankach, P., Lorino, T., & Gastineau, P. (2019). A constraint-based, efficiency optimization approach to network-level pavement maintenance management. Structure and Infrastructure Engineering, 15(11), 1450–1467. https://doi.org/10.1080/15732479.2019.1624787
  • Issa, A., Sammaneh, H., & Abaza, K. (2022). Modeling pavement condition index using cascade architecture: classical and neural network methods. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46(1), 483–495. https://doi.org/10.1007/s40996-021-00678-9
  • Jorge, D., & Ferreira, A. (2012). Road network pavement maintenance optimization using the HDM-4 pavement performance prediction models. International Journal of Pavement Engineering, 13(1), 39–51. https://doi.org/10.1080/10298436.2011.563851
  • Justo-Silva, R., Ferreira, A., & Flintsch, G. (2021). Review on machine learning techniques for developing pavement performance prediction models. Sustainability, 13(9), 5248. https://doi.org/10.3390/su13095248
  • Khavandi Khiavi, A., & Mohammadi, H. (2018). Multi-objective optimization in pavement management system using NSGA-II method. Journal of Transportation Engineering, Part B: Pavements, 144(2), 04018016. American Society of Civil Engineers (ASCE).
  • Kim, D. Y., Chi, S., & Kim, J. (2018). Selecting network-level project sections for sustainable pavement management in Texas. Sustainability, 10(3), 686. https://doi.org/10.3390/su10030686
  • Lethanh, N., & Adey, B. (2013). Use of exponential hidden Markov models for modelling pavement deterioration. International Journal of Pavement Engineering, 14(7), 645–654. https://doi.org/10.1080/10298436.2012.715647
  • Lethanh, N., Kaito, K., & Kobayashi, K. (2014). Infrastructure deterioration prediction with a Poisson hidden Markov model on time series data. Journal of Infrastructure Systems, 21(3), 04014051. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000242
  • Li, Y., Cheetham, A., Zaghloul, S., Helali, K., & Bekheet, W. (2006). Enhancement of Arizona pavement management system for construction and maintenance activities. Transportation Research Record: Journal of the Transportation Research Board, 1974(1), 26–36. https://doi.org/10.1177/0361198106197400104
  • Mandiartha, P., Duffield, C. F., Thompson, R. G., & Wigan, M. R. (2017). Measuring pavement maintenance effectiveness using Markov Chains analysis. Structure and Infrastructure Engineering, 13(7), 844–854. https://doi.org/10.1080/15732479.2016.1212901
  • Mathew, B. S., & Isaac, K. P. (2014). Optimization of maintenance strategy for rural road network using genetic algorithm. International Journal of Pavement Engineering, 15(4), 352–360. https://doi.org/10.1080/10298436.2013.806807
  • Meidani, H., & Ghanem, R. (2015). Random Markov decision processes for sustainable Infrastructure systems. Structure and Infrastructure Engineering, 11(5), 655–667. https://doi.org/10.1080/15732479.2014.893445
  • Miah, M. T., Oh, E., Chai, G., & Bell, P. (2020). An overview of the airport pavement management systems (APMS). International Journal of Pavement Research and Technology, 13(6), 581–590. https://doi.org/10.1007/s42947-020-6011-8
  • Mohamed, A. S., Abdel-Wahed, T. A., & Othman, A. M. (2019 Investigating the effect of corrective maintenance on the pavement life cycle and the optimal maintenance strategies [Paper presentation]. Cictp 2019 (pp. 811–822). https://doi.org/10.1061/9780784482292.073
  • Mohamed, A. S., Xiao, F., & Hettiarachchi, C. (2022). Project level management decisions in construction and rehabilitation of flexible pavements. Automation in Construction, 133, 104035. https://doi.org/10.1016/j.autcon.2021.104035
  • Note: a preprint of this paper is deposited via Research Square which can be accessed using the link: https://doi.org/10.21203/rs.3.rs-3046384/v1
  • Pérez-Acebo, H., Bejan, S., & Gonzalo-Orden, H. (2018). Transition probability matrices for flexible pavement deterioration models with half-year cycle time. International Journal of Civil Engineering, 16(9), 1045–1056. https://doi.org/10.1007/s40999-017-0254-z
  • Pérez-Acebo, H., Mindra, N., Railean, A., & Rojí, E. (2019). Rigid pavement performance models by means of Markov Chains with half-year step time. International Journal of Pavement Engineering, 20(7), 830–843. https://doi.org/10.1080/10298436.2017.1353390
  • Saha, P., & Ksaibati, K. (2016). A risk-based optimisation methodology for pavement management system of county roads. International Journal of Pavement Engineering, 17(10), 913–923. https://doi.org/10.1080/10298436.2015.1065992
  • Santos, J., Ferreira, A., & Flintsch, G. (2019). An adaptive hybrid genetic algorithm for Pavement management. International Journal of Pavement Engineering, 20(3), 266–286. https://doi.org/10.1080/10298436.2017.1293260
  • Santos, J., Ferreira, A., Flintsch, G., & Cerezo, V. (2018). A multi-objective optimization approach for sustainable pavement management. Structure and Infrastructure Engineering, 14(7), 854–868. https://doi.org/10.1080/15732479.2018.1436571
  • Sebaaly, P. E., Hand, A., Epps, J., & Bosch, C. (1996). Nevada’s approach to pavement management. Transportation Research Record: Journal of the Transportation Research Board, 1524(1), 109–117. https://doi.org/10.1177/0361198196152400113
  • Sholevar, N., Golroo, A., & Esfahani, S. R. (2022). Machine learning techniques for pavement condition evaluation. Automation in Construction, 136, 104190. https://doi.org/10.1016/j.autcon.2022.104190
  • Torres-Machí, C., Chamorro, A., Pellicer, E., Yepes, V., & Videla, C. (2015). Sustainable Pavement management: Integrating economic, technical, and environmental aspects in decision making. Transportation Research Record: Journal of the Transportation Research Board, 2523(1), 56–63. https://doi.org/10.3141/2523-07
  • Xiao, F., Chen, X., Cheng, J., Yang, S., & Ma, Y. (2022). Establishment of probabilistic prediction models for pavement deterioration based on Bayesian neural network. International Journal of Pavement Engineering, 24(2), 1–16. https://doi.org/10.1080/10298436.2022.2076854
  • Yamany, M. S., & Abraham, D. M. (2021). Hybrid approach to incorporate preventive maintenance effectiveness into probabilistic pavement performance models. Journal of Transportation Engineering, Part B: Pavements, 147(1), 04020077. https://doi.org/10.1061/JPEODX.0000227
  • Yamany, M. S., Abraham, D. M., & Labi, S. (2021). Comparative analysis of Markovian methodologies for modeling infrastructure system performance. Journal of Infrastructure Systems, 27(2), 04021003. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000604
  • Yang, J., Lu, J., Gunaratne, M., & Dietrich, B. (2006). Modeling crack deterioration of flexible pavements: comparison of recurrent Markov chains and artificial neural networks. Transportation Research Record: Journal of the Transportation Research Board, 1974(1), 18–25. https://doi.org/10.1177/0361198106197400103
  • Zhang, X., & Gao, H. (2012). Road maintenance optimization through a discrete-time semi-Markov decision process. Reliability Engineering & System Safety, 103, 110–119. https://doi.org/10.1016/j.ress.2012.03.011