International Roughness Index prediction model for flexible pavements

作者: Nader Abdelaziz , Ragaa T. Abd El-Hakim , Sherif M. El-Badawy , Hafez A. Afify

DOI: 10.1080/10298436.2018.1441414

关键词: International Roughness IndexRutSurface finishPerformance indicatorIndex (economics)Environmental scienceRide qualityStatisticsRoad user

摘要: AbstractInternational Roughness Index (IRI) is a pavement performance indicator which reflects not only the condition but also ride quality and comfort level of road users. The aim ...

参考文章(28)
Paul E. Keller, Kevin L. Priddy, Artificial Neural Networks: An Introduction ,(2005)
Halil Ceylan, Kasthurirangan Gopalakrishnan, Mustafa Birkan Bayrak, Neural Networks Applications in Pavement Engineering: A Recent Survey International journal of pavement research and technology. ,vol. 7, pp. 434- 444 ,(2014) , 10.6135/IJPRT.ORG.TW/2014.7(6).434
S D Kohn, R W Perera, ISSUES IN PAVEMENT SMOOTHNESS: A SUMMARY REPORT NCHRP Web-Only Document. ,(2002)
Mike Murphy, Moo Yeon Kim, Jorge A Prozzi, Maria Burton, Maintenance and Rehabilitation Project Selection Using Artificial Neural Networks Transportation Research Board 93rd Annual MeetingTransportation Research Board. ,(2014)
Kasthurirangan Gopalakrishnan, Marshall R. Thompson, Anshu Manik, Rapid Finite-Element Based Airport Pavement Moduli Solutions using Neural Networks Journal of Civil and Environmental Engineering. ,vol. 1, pp. 104- 112 ,(2007)
Turki I. Al-Suleiman (Obaidat), Adnan M.S. Shiyab, Prediction of Pavement Remaining Service Life Using Roughness Data—Case Study in Dubai International Journal of Pavement Engineering. ,vol. 4, pp. 121- 129 ,(2003) , 10.1080/10298430310001634834
Musharraf Zaman, Pranshoo Solanki, Ali Ebrahimi, Luther White, Neural Network Modeling of Resilient Modulus Using Routine Subgrade Soil Properties International Journal of Geomechanics. ,vol. 10, pp. 1- 12 ,(2010) , 10.1061/(ASCE)1532-3641(2010)10:1(1)
H. I. Park, G. C. Kweon, S. R. Lee, Prediction of Resilient Modulus of Granular Subgrade Soils and Subbase Materials using Artificial Neural Network Road Materials and Pavement Design. ,vol. 10, pp. 647- 665 ,(2009) , 10.1080/14680629.2009.9690218