IDEAS home Printed from https://ideas.repec.org/r/inm/orinte/v12y1982i6p5-21.html
   My bibliography  Save this item

A Statewide Pavement Management System

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Kuhn, Kenneth D. & Madanat, Samer M., 2005. "Model Uncertainty and the Management of a System of Infrastructure Facilities," University of California Transportation Center, Working Papers qt6c84b9b4, University of California Transportation Center.
  2. Bian, Zheyong & Bai, Yun & Douglas, W. Scott & Maher, Ali & Liu, Xiang, 2022. "Multi-year planning for optimal navigation channel dredging and dredged material management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
  3. Durango-Cohen, Pablo L., 2007. "A time series analysis framework for transportation infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 493-505, June.
  4. Durango-Cohen, Pablo L. & Madanat, Samer M., 2008. "Optimization of inspection and maintenance decisions for infrastructure facilities under performance model uncertainty: A quasi-Bayes approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(8), pages 1074-1085, October.
  5. Xinhua Mao & Changwei Yuan & Jiahua Gan, 2019. "Incorporating Dynamic Traffic Distribution into Pavement Maintenance Optimization Model," Sustainability, MDPI, vol. 11(9), pages 1-15, April.
  6. Gendreau, Michel & Soriano, Patrick, 1998. "Airport pavement management systems: an appraisal of existing methodologies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(3), pages 197-214, April.
  7. Zhang, Xueqing & Gao, Hui, 2012. "Road maintenance optimization through a discrete-time semi-Markov decision process," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 110-119.
  8. Kumar, Uday M & Bhat, Sanjay P. & Kavitha, Veeraruna & Hemachandra, Nandyala, 2023. "Approximate solutions to constrained risk-sensitive Markov decision processes," European Journal of Operational Research, Elsevier, vol. 310(1), pages 249-267.
  9. Li, Yuwei & Madanat, Samer, 2002. "A steady-state solution for the optimal pavement resurfacing problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(6), pages 525-535, July.
  10. Lee, Jinwoo & Madanat, Samer, 2015. "A joint bottom-up solution methodology for system-level pavement rehabilitation and reconstruction," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 106-122.
  11. Shi, Yue & Xiang, Yisha & Xiao, Hui & Xing, Liudong, 2021. "Joint optimization of budget allocation and maintenance planning of multi-facility transportation infrastructure systems," European Journal of Operational Research, Elsevier, vol. 288(2), pages 382-393.
  12. Gabriel Bazi & John Khoury & F. Jordan Srour, 2017. "Integrating Data Collection Optimization into Pavement Management Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(3), pages 135-146, June.
  13. Sathaye, Nakul & Madanat, Samer, 2012. "A bottom-up optimal pavement resurfacing solution approach for large-scale networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 520-528.
  14. Gu, Weihua & Ouyang, Yanfeng & Madanat, Samer, 2012. "Joint optimization of pavement maintenance and resurfacing planning," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 511-519.
  15. Sathaye, Nakul & Madanat, Samer, 2011. "A bottom-up solution for the multi-facility optimal pavement resurfacing problem," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 1004-1017, August.
  16. A Brint & J Bridgeman & M Black, 2009. "The rise, current position and future direction of asset management in utility industries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 106-113, May.
  17. Rita Justo-Silva & Adelino Ferreira & Gerardo Flintsch, 2021. "Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models," Sustainability, MDPI, vol. 13(9), pages 1-27, May.
  18. Papakonstantinou, K.G. & Shinozuka, M., 2014. "Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 214-224.
  19. Nakat, Z. & Madanat, S. & Farshidi, F. & Harvey, J., 2006. "Development of an Empirical-Mechanistic Model of Overlay Crack Progression using Data from the Washington State PMS Database," Institute of Transportation Studies, Working Paper Series qt0488k9kz, Institute of Transportation Studies, UC Davis.
  20. M Black & A T Brint & J R Brailsford, 2005. "A semi-Markov approach for modelling asset deterioration," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(11), pages 1241-1249, November.
  21. Pekka Mild & Ahti Salo, 2009. "Combining a Multiattribute Value Function with an Optimization Model: An Application to Dynamic Resource Allocation for Infrastructure Maintenance," Decision Analysis, INFORMS, vol. 6(3), pages 139-152, September.
  22. Kobayashi, K. & Kaito, K. & Lethanh, N., 2014. "A competing Markov model for cracking prediction on civil structures," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 345-362.
  23. Lee, Jinwoo & Madanat, Samer, 2014. "Joint optimization of pavement design, resurfacing and maintenance strategies with history-dependent deterioration models," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 141-153.
  24. Rafic Faddoul & Abdul-Hamid Soubra & Wassim Raphael & Alaa Chateauneuf, 2013. "Extension of dynamic programming models for management optimization from single structure to multi-structures level," Post-Print hal-01006860, HAL.
  25. Zhang, Le & Fu, Liangliang & Gu, Weihua & Ouyang, Yanfeng & Hu, Yaohua, 2017. "A general iterative approach for the system-level joint optimization of pavement maintenance, rehabilitation, and reconstruction planning," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 378-400.
  26. Seyedshohadaie, S. Reza & Damnjanovic, Ivan & Butenko, Sergiy, 2010. "Risk-based maintenance and rehabilitation decisions for transportation infrastructure networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(4), pages 236-248, May.
  27. Seites-Rundlett, William & Bashar, Mohammad Z. & Torres-Machi, Cristina & Corotis, Ross B., 2022. "Combined evidence model to enhance pavement condition prediction from highly uncertain sensor data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
  28. Durango, Pablo L. & Madanat, Samer M., 2002. "Optimal maintenance and repair policies in infrastructure management under uncertain facility deterioration rates: an adaptive control approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(9), pages 763-778, November.
  29. Madanat, S M & Durango, Pablo L, 2001. "Optimal maintenance and repair policies in infrastructure management under uncertain facility deterioration rates: an adaptive control approach," University of California Transportation Center, Working Papers qt8jz8h9fw, University of California Transportation Center.
  30. Memarzadeh, Milad & Pozzi, Matteo, 2016. "Value of information in sequential decision making: Component inspection, permanent monitoring and system-level scheduling," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 137-151.
  31. Ouyang, Yanfeng & Madanat, Samer, 2006. "An analytical solution for the finite-horizon pavement resurfacing planning problem," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 767-778, November.
  32. Yingnan Yang & Hongming Xie, 2021. "Determination of Optimal MR&R Strategy and Inspection Intervals to Support Infrastructure Maintenance Decision Making," Sustainability, MDPI, vol. 13(5), pages 1-10, March.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.