Influence of Pavement Structure, Traffic, and Weather on Urban Flexible Pavement Deterioration

[EN] Various studies have been recently conducted to predict pavement condition, but most of them were developed in a certain region where climate conditions were kept constant and/or the research focused on specific road distresses using single parameters. Thus, this research aimed at determining the influence of pavement structure, traffic demand, and climate factors on urban flexible pavement condition over time. To do this, the Structural Number was used as an indicator of the pavement capacity, various traffic and climate variables were defined, and the Pavement Condition Index was used as a surrogate measure of pavement condition. The analysis was focused on the calibration of regression models by using the K-Fold Cross Validation technique. As a result, for a given pavement age, pavement condition worsens as the Equivalent Single Axle Load and the Annual Average Height of Snow increased. Likewise, a cold Annual Average Temperature (5¿15 °C) and a large Annual Average Range of Temperature (20¿30 °C) encourage a more aggressive pavement deterioration process. By contrast, warm climates with low temperature variations, which are associated with low precipitation, lead to a longer pavement service life. Additionally, a new classification of climate zones was proposed on the basis of the weather influence on pavement deterioration.This research was funded by the Spanish Ministry of Science and Innovation, grant number RTC-2017-6148-7, with the European Regional Development Fund.Llopis-Castelló, D.; García-Segura, T.; Montalbán-Domingo, L.; Sanz-Benlloch, MA.; Pellicer, E. (2020). Influence of Pavement Structure, Traffic, and Weather on Urban Flexible Pavement Deterioration. Sustainability. 12(22):1-20. https://doi.org/10.3390/su122297171201222Hajj, 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. doi:10.3141/2150-05Santero, N. J., & Horvath, A. (2009). Global warming potential of pavements. Environmental Research Letters, 4(3), 034011. doi:10.1088/1748-9326/4/3/034011Pérez-Acebo, H., Linares-Unamunzaga, A., Abejón, R., & Rojí, E. (2018). Research Trends in Pavement Management during the First Years of the 21st Century: A Bibliometric Analysis during the 2000–2013 Period. Applied Sciences, 8(7), 1041. doi:10.3390/app8071041Prozzi, J. A., & Madanat, S. M. (2004). Development of Pavement Performance Models by Combining Experimental and Field Data. Journal of Infrastructure Systems, 10(1), 9-22. doi:10.1061/(asce)1076-0342(2004)10:1(9)Ragnoli, A., De Blasiis, M., & Di Benedetto, A. (2018). Pavement Distress Detection Methods: A Review. Infrastructures, 3(4), 58. doi:10.3390/infrastructures3040058Osorio, 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(1), 28-36. doi:10.3141/2455-04Loprencipe, G., Pantuso, A., & Di Mascio, P. (2017). Sustainable Pavement Management System in Urban Areas Considering the Vehicle Operating Costs. Sustainability, 9(3), 453. doi:10.3390/su9030453LTPP Data Analysis: Factors Affecting Pavement Smoothness. NCHRP Web Document 40http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w40-a.pdfArambula, 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(1), 25-31. doi:10.3141/2225-04Meegoda, J. N., & 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.1943-5436.0000682Pérez-Acebo, H., Mindra, N., Railean, A., & Rojí, E. (2017). Rigid pavement performance models by means of Markov Chains with half-year step time. International Journal of Pavement Engineering, 20(7), 830-843. doi:10.1080/10298436.2017.1353390Osorio-Lird, A., Chamorro, A., Videla, C., Tighe, S., & Torres-Machi, C. (2017). Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management. Structure and Infrastructure Engineering, 14(9), 1169-1181. doi:10.1080/15732479.2017.1402064Pérez-Acebo, H., Gonzalo-Orden, H., Findley, D. J., & Rojí, E. (2020). A skid resistance prediction model for an entire road network. Construction and Building Materials, 262, 120041. doi:10.1016/j.conbuildmat.2020.120041Pérez-Acebo, H., Linares-Unamunzaga, A., Rojí, E., & Gonzalo-Orden, H. (2020). IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work. Coatings, 10(2), 97. doi:10.3390/coatings10020097Dong, Q., Huang, B., & Richards, S. H. (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.1943-5436.0000738Hassan, R., Lin, O., & Thananjeyan, A. (2015). A comparison between three approaches for modelling deterioration of five pavement surfaces. International Journal of Pavement Engineering, 18(1), 26-35. doi:10.1080/10298436.2015.1030744Pérez-Acebo, H., Gonzalo-Orden, H., & Rojí, E. (2019). Skid resistance prediction for new two-lane roads. Proceedings of the Institution of Civil Engineers - Transport, 172(5), 264-273. doi:10.1680/jtran.17.00045Ziari, H., Maghrebi, M., Ayoubinejad, J., & Waller, S. T. (2016). Prediction of Pavement Performance: Application of Support Vector Regression with Different Kernels. Transportation Research Record: Journal of the Transportation Research Board, 2589(1), 135-145. doi:10.3141/2589-15Pérez-Acebo, H., Bejan, S., & Gonzalo-Orden, H. (2017). Transition Probability Matrices for Flexible Pavement Deterioration Models with Half-Year Cycle Time. International Journal of Civil Engineering, 16(9), 1045-1056. doi:10.1007/s40999-017-0254-zGarcía-Segura, T., Montalbán-Domingo, L., Llopis-Castelló, D., Lepech, M. D., Sanz, M. A., & Pellicer, E. (2020). Incorporating pavement deterioration uncertainty into pavement management optimization. International Journal of Pavement Engineering, 1-12. doi:10.1080/10298436.2020.1837827Qiao, Y., Flintsch, G. W., Dawson, A. R., & Parry, T. (2013). Examining Effects of Climatic Factors on Flexible Pavement Performance and Service Life. Transportation Research Record: Journal of the Transportation Research Board, 2349(1), 100-107. doi:10.3141/2349-12Mohd Hasan, M. R., Hiller, J. E., & You, Z. (2015). Effects of mean annual temperature and mean annual precipitation on the performance of flexible pavement using ME design. International Journal of Pavement Engineering, 17(7), 647-658. doi:10.1080/10298436.2015.1019504Anastasopoulos, P. C., & Mannering, F. L. (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.0000208Alaswadko, N., & Hassan, R. (2016). Rutting progression models for light duty pavements. International Journal of Pavement Engineering, 19(1), 37-47. doi:10.1080/10298436.2016.115512