Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management

References

• Ahmed, A., Labi, S., Li, Z., & Shields, T. (2013). Aggregate and disaggregate statistical evaluation of the performance-based effectiveness of long-term pavement performance specific pavement study-5 (LTPP SPS-5) flexible pavement rehabilitation treatments. Structure and Infrastructure Engineering, 9(2), 172–187. doi:10.1080/15732479.2010.524224
   Web of Science ®Google Scholar
• Amador-Jimenez, L. E., & Mrawira, D. (2011). Reliability-based initial pavement performance deterioration modelling. International Journal of Pavement Engineering, 12(2), 177–186. doi:10.1080/10298436.2010.53553810.1080/10298436.2010.535538
   Web of Science ®Google Scholar
• Amador-Jiménez, L. E., & Mrawira, D. (2011). Reliability-based initial pavement performance deterioration modelling. International Journal of Pavement Engineering, 12(2), 177–186. doi:10.1080/10298436.2010.535538
   Web of Science ®Google Scholar
• American Society for Testing and Materials (ASTM). (2003). Standard practice for roads and parking lots pavement condition index surveys ( No. ASTM D6433-11). West Conshohocken, PA: Author.
   Google Scholar
• Anastasopoulos, P., & Mannering, F. (2015). Analysis of pavement overlay and replacement performance using random parameters hazard-based duration models. Journal of Infrastructure Systems, 21(1), 04014024. doi:10.1061/(ASCE)IS.1943-555X.0000208
   Web of Science ®Google Scholar
• Arambula, E., George, R., Xiong, W., & Hall, G. (2011). Development and validation of pavement performance models for the state of Maryland. Transportation Research Record: Journal of the Transportation Research Board, 2225, 25–31. doi:10.3141/2225-04
   Web of Science ®Google Scholar
• Ariaratnam, S., El-Assaly, A., & Yang, Y. (2001). Assessment of infrastructure inspection needs using logistic models. Journal of Infrastructure Systems, 7(4), 160–165.
   Google Scholar
• Baik, H., Jeong, H., & Abraham, D. (2006). Estimating transition probabilities in Marko Chain – Based deterioration models for management of wastewater systems. Journal of Water Resources Planning and Management, 132(1), 15–24. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:1(15)
   Web of Science ®Google Scholar
• Black, M., Brint, A. T., & Brailsford, J. R. (2005). A semi-Markov approach for modelling asset deterioration. Journal of the Operational Research Society, 56(11), 1241–1249.10.1057/palgrave.jors.2601967
   Web of Science ®Google Scholar
• Brisbane City Council. (2008). Roads & traffic fact sheet. Road classification. Brisbane: Brisbane City Council.
   Google Scholar
• Chamorro, A. (2012). Development of a sustainable management system for rural road networks in developing countries ( PhD thesis). University of Waterloo, Canada.
   Google Scholar
• Chamorro, A., de Solminihac, H., Salgado, M., & Barrera, E. (2009). Development and validation of a method to evaluate unpaved road condition with objective distress measures. Transportation Research Record: Journal of the Transportation Research Board, 2101, 3–9. doi:10.3141/2101-01
   Google Scholar
• Chamorro, A., & Tighe, S. (2011). Condition performance models for network-level management of unpaved roads. Transportation Research Record, 2204, 21–28.10.3141/2204-03
   Web of Science ®Google Scholar
• Chamorro, A., & Tighe, S. (2015). Optimized maintenance standards for unpaved road networks based on cost-effectiveness analysis. Transportation Research Record: Journal of the Transportation Research Board, 2473, 56–64. doi:10.3141/2473-07
   Web of Science ®Google Scholar
• Chamorro, A., Tighe, S. L., Li, N., & Kazmierowski, T. J. (2009). Development of distress guidelines and condition rating to improve network management in Ontario, Canada. Transportation Research Record, 2093, 128–135.10.3141/2093-15
   Web of Science ®Google Scholar
• Chan, P., Oppermann, M., & Wu, S.-S. (1997). North Carolina’s experience in development of pavement performance prediction and modeling. Transportation Research Record: Journal of the Transportation Research Board, 1592, 80–88. doi:10.3141/1592-10
   Google Scholar
• Chu, C.-Y., & Durango-Cohen, P. (2007). Estimation of infrastructure performance models using state-space specifications of time series models. Transportation Research Part C: Emerging Technologies, 15, 17–32. doi:10.1016/j.trc.2006.11.004
   Web of Science ®Google Scholar
• Chu, C.-Y., & Durango-Cohen, P. (2008). Estimation of dynamic performance models for transportation infrastructure using panel data. Transportation Research Part B, 42, 57–81. doi:10.1016/j.trb.2007.06.004
   Google Scholar
• City of Toronto. (2013). City of Toronto, road classification system. Summary document. Transportation Infrastructure Management, Transportation Services Division. Toronto: City of Toronto.
   Google Scholar
• de Solminihac, H., Hidalgo, S. P., & Salgado, T. M. (2003). Calibration of performance models for surface treatment to Chilean conditions: The HDM-4 case. Transportation Research Record, 1819, 285–293.10.3141/1819b-36
   Google Scholar
• Department of Transport, Britain. (2012). Guidance on road classification and the primary route network. Department of Transport of Britain.
   Google Scholar
• DGAC & DMC. (2011). Anuario Climatológico 2010. Santiago, Chile: Dirección General de Aeronautica Civil y Dirección Meteorologica de Chile.
   Google Scholar
• Dong, Q., Huang, B., & Richards, S. (2015). Calibration and application of treatment performance models in a pavement management system in Tennessee. Journal of Transportation Engineering, 141(2), 04014076. doi:10.1061/(ASCE)TE.1943-5436.0000738
   Google Scholar
• Echaveguren, T. (2006). Investigación y desarrollo de procedimientos para la medicion y control de friccion superficial en pavimentos en Chile [Research and development of procedures for the measurement and control of surface friction in pavements in Chile]. Documento Técnico TM-022-06. Informe de Avance Actividad 8. Procedimientos de calibracion. Proyecto FONDEF D03I-1042 [Technical Memo TM-022-06. Report of Advance - Activity 8. Calibration Procedures. Proyect FONDEF D03I-1042]. Pontificia Universidad Católica De Chile.
   Google Scholar
• Ferreira, A., de Picado-Santos, L., Wu, Z., & Flintsch, G. (2011). Selection of pavement performance models for use in the Portuguese PMS. International Journal of Pavement Engineering, 12(1), 87–97. doi:10.1080/10298436.2010.506538
   Web of Science ®Google Scholar
• Flintsch, G. W., & Chen, C. (2004). Soft computing applications in infrastructure management. Journal of Infrastructure Systems, 10(4), 157–166. doi:10.1061/(ASCE)1076-0342(2004)10:4(157)
   Google Scholar
• Hassan, R., Lin, O., & Thananjeyan, A. (2015). A comparison between three approaches for modelling deterioration of five pavement surfaces. International Journal of Pavement Engineering, 1–10. doi:10.1080/10298436.2015.1030744
   Web of Science ®Google Scholar
• Jain, S. S., Aggarwal, S., & Parida, M. (2005). HDM-4 pavement deterioration models for Indian national highway network. Journal of Transportation Engineering, 131(8), 623–631. doi:10.1061/(ASCE)0733-947X(2005)131:8(623)
   Web of Science ®Google Scholar
• Kargah-Ostadi, N., Stoffels, S., & Tabatabaee, N. (2010). Network-level pavement roughness prediction model for rehabilitation recommendations. Transportation Research Record: Journal of the Transportation Research Board, 2155, 124–133. doi:10.3141/2155-14
   Web of Science ®Google Scholar
• Kerali, H. R., & Snaith, M. S. (1992). NETCOM: The TRL visual condition model for road networks. Wokingham: Transportation Research Laboratory.
   Google Scholar
• Khurshid, M. B., Irfan, M., Ahmed, A., & Labi, S. (2014). Multidimensional benefit-cost evaluation of asphaltic concrete overlays of rigid pavements. Structure and Infrastructure Engineering, 10(6), 792–810. doi:10.1080/15732479.2013.767842
   Web of Science ®Google Scholar
• La Torre, F., Ballerini, L., & Di Volo, N. (2002). Correlation between longitudinal roughness and user perception in urban areas. Transportation Research Record: Journal of the Transportation Research Board, 1806, 131–139. doi:10.3141/1806-15
   Google Scholar
• Lunn, D. J., Thomas, A., Best, N., & Spiegelhalter, D. (2000). WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing, 10(4), 325–337. doi:10.1023/A:1008929526011
   Web of Science ®Google Scholar
• Meegoda, J., & Gao, S. (2014). Roughness progression model for asphalt pavements using long-term pavement performance data. Journal of Transportation Engineering, 140(8), 04014037. doi:10.1061/(ASCE)TE.1943-5436.0000682
   Google Scholar
• Micevski, T., Kuckzera, G., & Coombes, P. (2002). Markov model for storm water pipe deterioration. Journal of Construction Infrastructure Systems, 8(2), 49–56.
   Google Scholar
• Mills, L., Attoh-Okine, N., & McNeil, S. (2012). Developing pavement performance models for Delaware. Transportation Research Record: Journal of the Transportation Research Board, 2304, 97–103. doi:10.3141/2304-11
   Google Scholar
• Ministerio de Urbanismo y Vivienda. (2008). Código de Normas y Especificaciones Técnicas de Obras de Pavimentación. Santiago: Author.
   Google Scholar
• Ministerio Obras Públicas de Chile (MOP), & DDQ Consultants. (2008). Modelos de deterioro de caminos no pavimentados. Santiago: Dirección de Vialidad, Ministerio de Obras Públicas de Chile.
   Google Scholar
• Montgomery, D. C., & Runger, G. C. (1994). Applied statistics and probability for engineers. New York, NY: Wiley.
   Google Scholar
• Odoki, J. B., & Kerali, H. R. G. (2006). Analytical framework and model descriptions – Volume 4 of highway development and management series. Paris: International Study of Highway Development and Management (ISOHDM), World Road Association PIARC.
   Google Scholar
• Ortiz-García, J. J., Costello, S. B., & Snaith, M. S. (2006). Derivation of transition probability matrices for pavement deterioration modeling. Journal of Transportation Engineering, 132(2), 141–161.10.1061/(ASCE)0733-947X(2006)132:2(141)
   Web of Science ®Google Scholar
• Osorio Lird, A. (2015). Development of performance models and maintenance standards of urban pavements for network management. Santiago: Pontificia Universidad Católica de Chile & University of Waterloo.
   Google Scholar
• Osorio, A., Chamorro, A., Tighe, S., & Videla, C. (2014). Calibration and validation of condition indicator for managing urban pavement networks. Transportation Research Record: Journal of the Transportation Research Board, 2455, 28–36. doi:10.3141/2455-04
   Web of Science ®Google Scholar
• Park, K., Thomas, N. E., & Lee, K. W. (2007). Applicability of the international roughness index as a predictor of asphalt pavement condition. Journal of Transportation Engineering, 133(12), 706–709. doi:10.1061/(ASCE)0733-947X(2007)133:12(706)
   Web of Science ®Google Scholar
• Pidwirny, M., & Jones, S. (2010). Fundamentals of physical geography ebook. Retrieved from http://www.physicalgeography.net/
   Google Scholar
• Rahim, A., Fiegel, G., Calkins, R., Lim, D., & Holland, J. (2013). Performance prediction models for cracked, seated, and overlaid concrete pavements in California. Transportation Research Record: Journal of the Transportation Research Board, 2368, 92–101. doi:10.3141/2368-09
   Google Scholar
• Rajagopalan, B., Lall, U., & Tarboton, D. (1996). No homogeneous Markov model for daily precipitation. Journal of Hydrological Engineering, 1(1), 36–40.
   Web of Science ®Google Scholar
• Sandra, A. K., & Sarkar, A. K. (2013). Development of a model for estimating International Roughness Index from pavement distresses. International Journal of Pavement Engineering, 14(8), 715–724. doi:10.1080/10298436.2012.703322
   Web of Science ®Google Scholar
• Shafizadeh, K., & Mannering, F. (2003). Acceptability of pavement roughness on urban highways by driving public. Transportation Research Record: Journal of the Transportation Research Board, 1860, 187–193. doi:10.3141/1860-21
   Google Scholar
• Silva, F., Van Dam, T., Bulleit, W., & Ylitalo, R. (2000). Proposed pavement performance models for local government agencies in Michigan. Transportation Research Record: Journal of the Transportation Research Board, 1699, 81–86. doi:10.3141/1699-11
   Google Scholar
• Tack, J., & Chou, Y. (2001). Pavement performance analysis applying probabilistic deterioration methods. Transportation Research Record: Journal of the Transportation Research Board, 1769, 20–27. doi:10.3141/1769-03
   Google Scholar
• Tao, Z., Corotis, R., & Ellis, H. (1995). Reliability – based structural design with markov decision processes. Journal of Structural Engineering, 121(6), 971–980.
   Web of Science ®Google Scholar
• Thomas, O., & Sobanjo, J. (2013). Comparison of Markov chain and semi-markov models for crack deterioration on flexible pavements. Journal of Infrastructure Systems, 19(2), 186–195 https://doi-org.ezproxy.puc.cl/10.1061/(ASCE)IS.1943-555X.000011210.1061/(ASCE)IS.1943-555X.0000112
   Web of Science ®Google Scholar
• Thube, D. T. (2013). Highway development and management model (HDM-4): Calibration and adoption for low-volume roads in local conditions. International Journal of Pavement Engineering, 14(1), 50–59. doi:10.1080/10298436.2011.606320
   Web of Science ®Google Scholar
• Tjan, A., & Pitaloka, D. (2005). Future prediction of pavement condition using Markov probability transition matrix (Vol. 5, pp. 772–782). Presented at the Eastern Asia Society for Transportation Studies.
   Google Scholar
• Transport for London. (2015). The vision and direction for London’s streets and roads. London: Roads Task Force.
   Google Scholar
• Transportation Association of Canada. (2013). Pavement asset design and management guide. Ontario: Author.
   Google Scholar
• Videla, C., Chamorro, A., Mourgues, C., & Gillmore, R. (2010). Investigación y Desarrollo de Soluciones para la Gestión de Pavimentos Urbanos en Chile (Reseach project proposal). Santiago: Pontificia Universidad Católica de Chile.
   Google Scholar
• Yang, J., Gunaratne, M., Lu, J., & Dietrich, B. (2005). Use of recurrent Markov chains for modeling the crack performance of flexible pavements., 131(11), 861–872.
   Google Scholar
• 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, 18–25. doi:10.3141/1974-05
   Google Scholar