Predicting a Pavement Roughness on the Basis of PCI Data

Document Type : Research Paper

Authors

Department of Civil Engineering, Payame Noor University (PNU), P.O.Box 19395-4697, Tehran, Iran

Abstract

Pavement deterioration is a factor contributing to the higher roughness of the road and lower driving comfort in road trips. The pavement roughness measurement has long been an especially important topic to the practitioners. The pavement condition index (PCI) and the international roughness index (IRI) have been applied as two key measures of the quality and performance of the road pavement at the level of project or the road network. Since the pavement condition assessment across a road network by means of the PCI is highly expensive, the present research aims at estimating the PCI based on the IRI to save time and money while evaluating the pavement condition across a road network. For this purpose, we considered three IRI and PCI measurements over main suburban roads in a cold-weather area during a period of 6 years. Firstly, the independence of errors was controlled using the Durbin – Watson statistic, followed by evaluating the normality of the data using the Kolmogorov – Smirnov and the Shapiro – Wilk tests. In order to measure the correlation of the normal data, we used the Pearson’s tests. Results of regression analysis between IRI and PCI showed that the best equation describing their association is a linear inverse model with an R2 = 0.992. A statistical comparison was made between the observed and predicted PCI values and model developed in the present research was further compared to similar research works, exhibiting acceptable agreement of the results.

Keywords

References

-ASTM D6433(1999), Standart Practice for Roads and Parking Lots Pavement Condition Index Surveys
-Akhter, M., Boyer, J., Hancock, M., Hossain, M.(2003) “Evaluation of Performance of Light-Weight Profilometers”. Final Report. FHWA-KS-01-2. Kansas DOT
-AASHTO.(2008) “Standard specifications for transportation materials and methods of sampling and testing”. Washington DC: 2008.
-Arhin,A.,Williams,L.,Ribbiso,A.,Anderson,M. (2015) “Predicting Pavement Condition Index Using International Roughness Index in a Dense Urban Area” Journal of Civil Engineering Research, 5(1): 10-17.
-A.Putra, Donny., Suprapto, Mamok.(2018) “Assessment of the road based on PCI and IRI roadroid measurement” MATEC Web of Conferences195,04006,https://doi.org/10.1051/matecconf/201819504006
-ASTM E1926 - 08(2021) “Standard Practice for Computing International Roughness Index of Roads from Longitudinal Profile Measurements”.https://www.astm.org/Standards/E1926.
-Adeli,S. Najafi moghaddam Gilani,V.Kashani Novin,M. Motesharei,E. Salehfard,R(2021) “Development of a Relationship between Pavement Condition Index and International Roughness Index in Rural Road Network”, Hindawi Advances in Civil Engineering, Article ID6635820, 9 pages https://doi.org/10.1155/202 1/6635820.
- Arnastauskait˙e, J., Ruzgas, T., Mindaugas, B.(2021) “An Exhaustive Power Comparison of Normality Tests” Mathematics 2021, 9, 788. https://doi.org/10.3390/math90707 88
-Du, Y., Liu, ch., Wu, D., Jiang,Sh.(2014) “Measurement of International Roughness Index by Using-Axis Accelerometers and GPS”. Mathematical Problems in Engineering.
-Deori, S., Choudhary,R, Tiwari, D., Kumar, A. (2019). “HDM-4 deterioration modelling: validation and adoption for flexible pavements with modified bituminous road surfacing” the Baltic journal of road and bridge engineering, Volume 14 Issue 2: 208–226
-Gillespie, T.D.(1980) “Calibration of response-type road roughness measuring systems”.
-Huang, Yang h,(2004) “Pavement analysis and design,2nd". Pearson
- Hafez M, Ksaibati K, Atadero R (2019) Best practices to support and improve pavement management systems for low-volume paved roads. Int J Pavement Eng 20(5):592–599
- Ibrahim, E. M., El‑Badawy, S.M., Ibrahim, M. H., Elbeltagi, Emad.,(2020) “A modified pavement condition rating index for flexible pavementevaluation in Egypt” Springer Nature Switzerland AG 2020, https://doi.org/10.1007/s41062-020-00304-z
-Jannat GE, Tighe SL (2015) Performance based evaluation of overall pavement condition indices for Ontario highway systems.In: TAC 2015: Getting you there safely-2015 conference and exhibition of the transportation association of Canada//ATC: session of the 2015 conference of the transportation, EKP,vol 13, no 3, pp 1576–1580
-John P. Hoffmann(2021). “Linear Regression Models” Chapman and Hall/CRC; 1st edition
-Mahmoudzadeh, A., Golroo,A.,Jahanshahi,M., Firoozi Y,S.(2019) “Estimating pavement roughness by fusing color and depth data obtained from an inexpensive RGB-D sensor,” Sensors, vol. 19, no. 7, p. 1655.
-Ministry of roads and urban development, road maintenance and transportation organization(RMTO), department of road maintenance, center of data mining and transportation infrastructures management,2020
-Nam BH, An J, Kim M, Murphy MR, Zhang Z (2016) Improvements to the structural condition index (SCI) for pavement structural evaluation at network level. Int J Pavement Eng 17(8):680 6976.
-Naaman, Michael (2021). "On the tight constant in the multivariate Dvoretzky-Kiefer-Wolfowitz inequality". Statistics and ProbabilityLetters. 173:109088. doi:10.1016/j.spl.2021.109088. S2CID 233844405 – via Science Direct
-ODOT(The Ohio Department of Transportation) (2004) Pavement condition rating system 4(10):1–3
-Oleh Anggraini.,Siti wahyupa(2019) “The effect of gender on genre preferences and reading achievement to ninth grade students’ of junior high school”.Vol.14 No.4 Nopember2019. http://ejurnal.binawakya.or.id/index.php/MBI
-Papagiannakis, A.,Gharaibeh,N., Weissmann,J., Wimsatt A (2009) Pavement scores synthesis. Project FHWA 0-6386 Report No.FHWA/TX-09/0-6386-1, 7(2), 152.
-Piryonesi,S. Madeh.,El-Diraby, Tamber E. (2018) “Data Analytics for Cost-Effective Prediction of Road Conditions: Case of the Pavement Condition Index”. Washington DC: 2018.
-Piryonesi, S. M (2019) “The Application of Data Analytics to Asset Management: Deterioration and Climate Change Adaptation in Ontario Roads”. University of Toronto.
 -Piryonesi, S.M and El-Diraby, T.(2020) “Examining the Relationship Between Two Road Performance Indicators: Pavement Condition Index and International Roughness Index”, Transportation Geotechnics, doi: https://doi.org /10.1016/j.trgeo.2020.100441
-Piryonesi, S.M., El-Diraby, T.(2020) “Data Analytics in Asset Management: Cost-EffectivePrediction of the Pavement Condition Index”.J Infrastruct Syst 2020;26:04019036 .https://doi.org/10.1061/(ASCE)IS.1943-555X.0 000512.http://tti.tamu.edu/docum ents/0-6386-1.pdf
-Sayers, MW., Gillespie, TD., Paterson, WD.(1986) “Guidelines for Conducting and Calibrating Road Roughness Measurements” World Bank Tech Pap No 46.
-Sayers, M.W.,  S.M. Karamihas(1998) “The little book of profiling”. University of Michigan.
-Shahin,Mohamed Y (2006) “Pavement Management for Airports, Roads, and Parking Lots 2nd Edition”. Springer.
-Sharma, Rajesh., Bhandari, Rajeev K. (2015), "Skewness, kurtosis and Newton's inequality", Rocky Mountain Journal of Mathematics, 45 (5):16391643, doi:10.1216/RMJ-2015-45-5-1639, S2CID 88513237
-Smith, K., Ram, P.(2016) “Measuring and Specifying Pavement Smoothness”. New Jersey.
-Zavagna, P., Khanal, A., Souliman, M.(2018) “LTTP Data Analysis: Factors Affecting Pavement Roughness for the State of California” jurnal of materials and engineering structures5 (2018) 319–332.‏

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