Kalman filter updating of rutting predictive models in flexible pavements using measured field data

References

• AASHTO, 2008. Guide specifications for highway construction. 9th ed. American Association of State Highway and Transportation Officials (AASHTO). https://books.google.com.lb/books?id=E4MzYL7S0AMC.
   Google Scholar
• AASHTO, 2012. Pavement management guide. 2nd ed. Washington, DC: American Association of State Highway and Transportation Officials.
   Google Scholar
• AASHTO, 2015. Mechanistic-empirical pavement design guide: a manual of practice. Washington, DC: American Association of State Highway and Transportation Officials. https://books.google.co.il/books?id=dduLtAEACAAJ.
   Google Scholar
• Abambres, M., and Ferreira, A., 2018. Application of ANN in pavement engineering: state-of-art. ViXra. doi:10.31224/osf.io/nh8k6.
   Google Scholar
• Abaza, K.A., 2016. Simplified staged-homogenous Markov model for flexible pavement performance prediction. Road Materials and Pavement Design, 17, 365–381.
   Web of Science ®Google Scholar
• Abu-Ennab, L.B., 2015. Developing pavement performance prediction models for the state of Arkansas. Little Rock, Arkansas: University of Arkansas at Little Rock.
   Google Scholar
• Amador-Jiménez, L., and Mrawira, D., 2012. Bayesian regression in pavement deterioration modeling: revisiting the AASHO road test Rut depth model. Infraestructura Vial, 14, 28–35.
   Google Scholar
• ASCE, 2017. 2017 infrastructure report card. Reston, VA: ASCE.
   Google Scholar
• ASTM, ASTM D6433-20, 2020. Standard practice for roads and parking lots pavement condition index surveys. doi:10.1520/D6433-20.
   Google Scholar
• Baladi, G.Y., et al., 2017. Pavement performance measures and forecasting and the effects of maintenance and rehabilitation strategy on treatment effectiveness. Maclean, Virginia: Federal Highway Administration.
   Google Scholar
• Bannour, A., et al., 2019. Optimization of the maintenance strategies of roads in Morocco: calibration study of the degradations models of the highway development and management (HDM-4) for flexible pavements. International Journal of Pavement Engineering, 20, 245–254.
   Web of Science ®Google Scholar
• Bertino, L., Evensen, G., and Wackernagel, H., 2003. Sequential data assimilation techniques in oceanography. International Statistical Review, 71, 223–241.
   Web of Science ®Google Scholar
• Bichara, L., Saad, G., and Slika, W., 2019. Probabilistic identification of the effects of corrosion propagation on reinforced concrete structures via deflection and crack width measurements. Materials and Structures, 52, 89. doi:10.1617/s11527-019-1389-y.
   Web of Science ®Google Scholar
• Choi, S., and Do, M., 2020. Development of the road pavement deterioration model based on the deep learning method. Electronics, 9. doi:10.3390/electronics9010003.
   PubMed Web of Science ®Google Scholar
• Choummanivong, L., and Martin, T., 2010. Interim network level functional road deterioration models, Austroads Publ. No. AP–T158/10, ARRB.
   Google Scholar
• Di Lorenzo, E., et al., 2008. Theory and applications of variational, sequential and Bayesian hierarchical methods in data assimilation. AGU Fall Meeting Abstract.
   Google Scholar
• Ekblad, J., et al., 2021. Impact on rutting from introduction of increased axle loads in Finland. International Journal of Pavement Engineering, 1731–1743. doi:10.1080/10298436.2020.1721497.
   Web of Science ®Google Scholar
• Elkins, G.E., et al., 2018. Long-term pavement performance information management system user guide. McLean, VA. https://trid.trb.org/view.aspx?id=697916
   Google Scholar
• Evensen, G., 2002. Sequential data assimilation for nonlinear dynamics: the ensemble Kalman filter, Ocean forecast. Berlin, Heidelberg: Springer, 97–116.
   Google Scholar
• Evensen, G., 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dynamics, 53, 343–367. doi:10.1007/s10236-003-0036-9.
   Google Scholar
• FHWA, 2018. Transportation asset management plan development processes certification and recertification guidance. Washington, DC. Available from: https://www.fhwa.dot.gov/asset/guidance/certification.pdf (Accessed 20 Feb 2020].
   Google Scholar
• Fwa, F.T., Pasindu, H. R., and Ong, G. P., 2012. Critical rut depth for pavement maintenance based on vehicle skidding and hydroplaning consideration. Journal of Transportation Engineering, 138, 423–429. doi:10.1061/(ASCE)TE.1943-5436.0000336.
   Web of Science ®Google Scholar
• George, K.P., 2000. MDOT pavement management system: prediction models and feedback system. Mississippi: Dept. of Transportation.
   Google Scholar
• Gillijns, S., et al., 2006. What is the ensemble Kalman filter and how well does it work? In: 2006 American Control Conf., IEEE, 6.
   Google Scholar
• Gong, H., et al., 2018. Improving accuracy of rutting prediction for mechanistic-empirical pavement design guide with deep neural networks. Construction and Building Materials, 190, 710–718.
   Web of Science ®Google Scholar
• Gonzalo, R., et al., 2018. Network-level pavement structural evaluation. Journal of Infrastructure Systems, 24, 04018033. doi:10.1061/(ASCE)IS.1943-555X.0000454.
   Web of Science ®Google Scholar
• Haddad, A.J., Chehab, G.R., and Saad, G.A., 2021. The use of deep neural networks for developing generic pavement rutting predictive models. International Journal of Pavement Engineering, 1–17. doi:10.1080/10298436.2021.1942466.
   Google Scholar
• Hjelle, H.M., 2007. A model for estimating road wear on in-service roads. International Journal of Pavement Engineering, 8, 237–244. doi:10.1080/10298430600677453.
   Google Scholar
• Hong, F., and Prozzi, J.A., 2006. Estimation of pavement performance deterioration using Bayesian approach. Journal of Infrastructure Systems, 12, 77–86.
   Web of Science ®Google Scholar
• Inkoom, S., et al., 2020. Assessment of deterioration of highway pavement using Bayesian survival model. Transportation Research Record: Journal of the Transportation Research Board, 2674, 310–325. doi:10.1177/0361198120919112.
   Web of Science ®Google Scholar
• Jain, S.S., Parida, M., and Thube, D.T., 2007. HDM-4 based optimal maintenance strategies for low-volume roads in India. Road & Transport Research, 16, 3.
   Google Scholar
• Jeyapalan, K., Cable, J.K., and Welper, R., 1987. Iowa DOT evaluation of the PASCO road survey system.
   Google Scholar
• Jiménez, L.A., and Mrawira, D., 2012. Bayesian regression in pavement deterioration modeling: revisiting the AASHO road test rut depth model. Transportation Research Record, 28–35.
   Google Scholar
• Khan, M.U., et al., 2014. Developing a new road deterioration model incorporating flooding. Proceedings of the Institution of Civil Engineers – Transport, 167 (5), 322–333 .
   Web of Science ®Google Scholar
• Li, N., Haas, R., and Xie, W.-C., 1997. Development of a new asphalt pavement performance prediction model. Canadian Journal of Civil Engineering, 24, 547–559.
   Web of Science ®Google Scholar
• Li, Q.J., et al., 2019. Data needs and implementation of the pavement ME design. Transportmetrica A: Transport Science, 15, 135–164. doi:10.1080/23249935.2018.1504254.
   Web of Science ®Google Scholar
• LTPP InfoPave, n.d. Available from: https://infopave.fhwa.dot.gov/?Goto=Home [Accessed 15 Dec 2020].
   Google Scholar
• Luo, Z., et al., 2016. Bayesian updating approach for flexible pavements considering fatigue and rutting failures. Journal of Testing and Evaluation, 44, 20140356–20140533. doi:10.1520/JTE20140356.
   Web of Science ®Google Scholar
• Mamlouk, M., et al., 2018. Effects of the international roughness index and rut depth on crash rates. Transportation Research Record: Journal of the Transportation Research Board, 2672, 418–429. doi:10.1177/0361198118781137.
   Web of Science ®Google Scholar
• National Academies of Sciences, Engineering, and Medicine. 2004. Automated pavement distress collection techniques. Washington, DC: The National Academies Press.
   Google Scholar
• Osorio-Lird, A., et al., 2018. Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management. Structure and Infrastructure Engineering, 14, 1169–1181. doi:10.1080/15732479.2017.1402064.
   Web of Science ®Google Scholar
• Pais, J.C., Amorim, S. I. R., and Minhoto, M.J.C., 2013. Impact of traffic overload on road pavement performance. Journal of Transportation Engineering, 139, 873–879. doi:10.1061/(ASCE)TE.1943-5436.0000571.
   Web of Science ®Google Scholar
• Park, E.S., et al., 2008. A Bayesian approach for improved pavement performance prediction. Journal of Applied Statistics, 35, 1219–1238.
   Web of Science ®Google Scholar
• Pérez-Acebo, H., et al., 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, 1041.
   Google Scholar
• Pham, D.T., 2001. Stochastic methods for sequential data assimilation in strongly nonlinear systems. Monthly Weather Review, 129, 1194–1207.
   Web of Science ®Google Scholar
• Pierce, L.M., McGovern, G., and Zimmerman, K.A., 2013. Practical guide for quality management of pavement condition data collection. Washington, DC: United States Federal Highway Administration.
   Google Scholar
• Pulugurta, H., Shao, Q., and Chou, Y.J., 2013. Pavement condition prediction using Markov process. Journal of Statistics and Management Systems, 12, 853–871. doi:10.1080/09720510.2009.10701426.
   Google Scholar
• Saha, P., Ksaibati, K., and Atadero, R., 2017. Developing pavement distress deterioration models for pavement management system using markovian probabilistic process. Advances in Civil Engineering, 2017, 1–9. doi:10.1155/2017/8292056.
   Web of Science ®Google Scholar
• Salama, H.K., Chatti, K., and Lyles, R.W., 2006. Effect of heavy multiple axle trucks on flexible pavement damage using In-service pavement performance data. Journal of Transportation Engineering, 132, 763–770. doi:10.1061/(ASCE)0733-947X(2006)132:10(763).
   Web of Science ®Google Scholar
• Simpson, A.L., 1999. Characterization of transverse profile. Transportation Research Record: Journal of the Transportation Research Board, 1655, 185–191.
   Google Scholar
• Slika, W., and Saad, G., 2016. An ensemble Kalman filter approach for service life prediction of reinforced concrete structures subject to chloride-induced corrosion. Construction and Building Materials, 115, 132–142. doi:10.1016/j.conbuildmat.2016.04.025.
   Web of Science ®Google Scholar
• Tabatabaee, N., and Ziyadi, M., 2013. Bayesian approach to updating markov-based models for predicting pavement performance. Transportation Research Record: Journal of the Transportation Research Board, 2366, 34–42. doi:10.3141/2366-04.
   Web of Science ®Google Scholar
• Thube, D.T., 2012. Artificial Neural Network (ANN) based pavement deterioration models for low volume roads in IndiaInternational Journal of Pavement Research and Technology, 5, 115–120.
   Google Scholar
• TRIP, 2016. The interstate highway system turns 60: Challenges to its ability to continue to save lives, time and money. Washington, DC. https://www.infrastructureusa.org/the-interstate-highway-system-turns-60-challenges-to-its-ability-to-continue-to-save-lives-time-and-money/ [accessed 20 Feb 2020].
   Google Scholar
• Underwood, B.S., et al., 2017. Increased costs to US pavement infrastructure from future temperature rise. Nature Climate Change, 7, 704–707. doi:10.1038/nclimate3390.
   Web of Science ®Google Scholar
• Von Quintus, H.L., Mallela, J., Bonaquist, R., Schwartz, C.W., and Carvalho, R.L., 2012. Calibration of rutting models for structural and mix design. NCHRP Report.
   Google Scholar
• Wang, D., et al., 2017. Influence of computation algorithm on the accuracy of rut depth measurement. Journal of Traffic and Transportation Engineering (English Edition), 4, 156–164. doi:10.1016/j.jtte.2017.03.001.
   Google Scholar
• Wang, Z., et al., 2021. Prediction of highway asphalt pavement performance based on Markov chain and artificial neural network approach. The Journal of Supercomputing, 77, 1354–1376. doi:10.1007/s11227-020-03329-4.
   Web of Science ®Google Scholar
• White, T.D., NCHR Program, A.A. of S.H. and T. Officials, N.R.C. (U. S.). T.R. Board, 2002. Contributions of pavement structural layers to rutting of hot mix asphalt pavements. National Academy Press. https://books.google.com.lb/books?id=4ysP-dp-QPAC.
   Google Scholar
• Wolters, A.S., and Zimmerman, K.A., 2010. Current practices in pavement performance modeling.
   Google Scholar
• Xiong, H., et al., 2012. A compromise programming model for highway maintenance resources allocation problem. Mathematical Problems in Engineering, 2012, 1. doi:10.1155/2012/178651.
   Web of Science ®Google Scholar
• Yamany, Mohamed S., and Abraham, Dulcy M., 2021. Hybrid approach to incorporate preventive maintenance effectiveness into probabilistic pavement performance models. Journal of Transportation Engineering, Part B: Pavements, 147, 04020077. doi:10.1061/JPEODX.0000227.
   Web of Science ®Google Scholar
• Yang, J., et al., 2003. Overall pavement condition forecasting using neural networks—an application to florida highway network. In: 82nd Annu. Meet. Transp. Res. Board.
   Google Scholar
• Yao, L., et al., 2019. Establishment of prediction models of asphalt pavement performance based on a novel data calibration method and neural network. Transportation Research Record. doi:10.1177/0361198118822501.
   Web of Science ®Google Scholar
• Zhang, Z., and Gao, L., 2018. A nested modelling approach to infrastructure performance characterisation. International Journal of Pavement Engineering, 19, 174–180. doi:10.1080/10298436.2016.1172712.
   Web of Science ®Google Scholar
• Zhou, F., and Scullion, T., 2002. Discussion: three stages of permanent deformation curve and rutting model. International Journal of Pavement Engineering, 3, 251–260. doi:10.1080/1029843021000083676.
   Google Scholar
• Zimmerman, K.A., and Ram, P. V, 2015. Pavement management’s role in an asset management world. In: 9th Int. Conf. Manag. Pavement Assets.
   Google Scholar